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Development and evaluation of a multiple-pest, production situation specific model to simulate yield losses of rice in tropical Asia
Abstract
A yield loss simulation model for rice was developed to simulate injury mechanisms due to pathogens, insects, and weeds, and the yield losses they cause in a range of production situations. The structure of the model is simple, flexible, and involves as few parameters as possible. The model consists of two linked components. The first simulates the dynamics of the rice crop, with accumulation of biomass and its daily partitioning towards leaves, stems, roots, and panicles. The second component simulates the dynamics of tillering, tiller maturation, panicle formation, and tiller death. Coupling functions representing damage mechanisms due to sheath blight, stem borers, and weeds were developed and parameterized from published and experimental data. Each of these injuries corresponds to a set of damage mechanisms, some of which are specific to the injury considered, while others are common to several injuries. The parameters required to simulate attainable growth and attainable yield were determined, using specific field experiments, under three different production situations representing those commonly occurring in the Philippines and in Vietnam. Yield loss simulations due to the different injuries, considered alone or in combination, were tested under these different production situations. The model accurately simulated attainable rice growth and development, and adequately accounted for the yield-reducing effects of the different injury mechanisms considered. Results from sensitivity analyses conducted at varying levels of injuries are discussed. This model can be used as a tool to set research priorities for novel plant protection strategies for rice in tropical Asia.
... Using crop growth models that are specifically designed to incorporate damage mechanisms (Savary and Willocquet, 2014), the approach has been used to model yield losses caused by injury profiles both in rice (Willocquet et al., 2000(Willocquet et al., , 2002(Willocquet et al., , 2004 and wheat (Willocquet et al., 2008. These analyses have enabled the assessment of disease and pest impacts and the framing of research priorities at a range of scales, from the individual farmer's field to the eco-region (e.g., tropical Asia for rice and Western Europe for winter wheat). ...
... These analyses have enabled the assessment of disease and pest impacts and the framing of research priorities at a range of scales, from the individual farmer's field to the eco-region (e.g., tropical Asia for rice and Western Europe for winter wheat). The yield loss process-based simulation models, WHEATPEST for wheat (Willocquet et al., 2008) and RICEPEST for rice (Willocquet et al., 2000(Willocquet et al., , 2002, account for 13 and 11 yield-reducing factors (diseases insect pests, and weeds), respectively. ...
... All these four models include a water balance module, and three of them (DSSAT-Nwheat, HERMES, SSM-WHEAT) include a nitrogen balance module. WHEATPEST, on the other hand, belongs to a class of yield loss models (Willocquet et al., 2000(Willocquet et al., , 2002(Willocquet et al., , 2004(Willocquet et al., , 2008Savary et al., 2006;Savary and Willocquet, 2014), which are specifically designed to incorporate the individual or combined growth-and yield-reducing effects of injuries by crop diseases, insect pests, and weeds. WHEATPEST furthermore includes features enabling the analysis of such reductions under a range of production situations, i.e., under a range of production contexts corresponding to different levels of attainable yields (Savary et al., 2006;Willocquet et al., 2004Willocquet et al., , 2008, which would be achieved in absence of pathogen, pest, or weed injuries. ...
Plant diseases are major causes of crop yield losses globally, yet their effects represent a poorly documented source of uncertainty in crop modelling. Ignoring the effects of plant diseases in crop models may lead to large overestimations of current and future crop production levels. Simulation modelling must be seen as a necessary instrument to understand systems and predict their behaviours. This instrument is therefore necessary when profound changes in system structures are envisioned in view of, e.g., ecological intensification or climate adaptation, which necessarily will change injury profiles by plant pathogens and pests. Here, damage mechanisms associated with four major diseases of wheat (brown and yellow rust, septoria tritici blotch and powdery mildew) are considered. These diseases and their damage mechanisms are featured in WHEATPEST, a process-based model for wheat growth under disease. The same damage mechanisms were thus incorporated into four wheat growth models (HERMES, WOFOST_GT, SSM_WHEAT, DSSAT-Nwheat), which did not account for yield losses to diseases before. A benchmark experimental data set from the Netherlands was used to perform two calibration steps to simulate disease-free attainable wheat yields (Ya), first by using the experimentally measured crop development and yields as reference, and second by further using the observed leaf area dynamics. A simulation experiment was then conducted with the five models, with three independent factors: (i) each of the four wheat diseases (individually or combined), (ii) the shape of disease progress curves, and (iii) the maximum disease severity. We analysed the simulated crop growth, actual crop yield (Y), and absolute (YL = Ya – Y) and relative (RYL = YL/Ya) yield loss, at different levels of these three factors. In a last stage of analysis, we simulated the effects of Ya on YL and RYL. Maximum severity of disease had the strongest effect on simulated Y and on YL, while there were also significant differences among models in the simulated YL. Powdery mildew and brown rust were generally associated with higher and lower YL, respectively. Simulated RYL increased as the Ya was decreased. Increase of RYL at lower Ya was attributed to a larger reduction of intercepted radiation at low green leaf area index. This work outlines the rationale for implementing damage mechanisms associated with plant diseases into crop models, and provides the necessary first step towards scenario analyses where the consequences of technology shifts, climate change, and changes in disease patterns may influence the magnitude of yield losses to plant diseases.
... This approach has been followed in the case of the rice-multiple pest system in Asia, where the successive steps above have been documented (Savary et al., 2000a(Savary et al., , 2000bWillocquet et al., 2000Willocquet et al., , 2002Willocquet et al., , 2004. It also has been implemented in the case of the wheat-multiple pest system in Western Europe, using extensive, published survey work in the Netherlands (Daamen, 1990;Daamen and Stol, 1990, 1992Daamen et al., 1991, and the UK (King, 1977;Polley and Thomas, 1991;Foster et al., 2004), as well as a large body of published parameters on damage mechanisms in the wheatmultiple pest system (Willocquet et al., 2008). ...
... Although field observations of pest and disease impacts on crops have been widely collected for many years (e.g., Nutter, 1989;Esker et al., 2012), measurement methods lack standardization, and usually are not linked with weather or agronomic data to enable their use as inputs for PDM. As a consequence, the extensive validation of PDM across diverse environments has been limited to very few cases (e.g., Willocquet et al., 2000Willocquet et al., , 2002Willocquet et al., , 2004. Consequently, there is a need to design protocols which can guide the collection of the experimental data needed to calibrate and evaluate PDM and crop loss models, including both epidemiological and crop data (see e.g., Willocquet et al., 2000), as summarized in the section Data requirements. ...
... As a consequence, the extensive validation of PDM across diverse environments has been limited to very few cases (e.g., Willocquet et al., 2000Willocquet et al., , 2002Willocquet et al., , 2004. Consequently, there is a need to design protocols which can guide the collection of the experimental data needed to calibrate and evaluate PDM and crop loss models, including both epidemiological and crop data (see e.g., Willocquet et al., 2000), as summarized in the section Data requirements. We propose here a tentative distinction between high (HQ) and medium (MQ) quality reference datasets for model calibration and evaluation, according to the typology of the variables to be measured and to the frequency of their sampling during the growing season. ...
The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects.A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs ; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.
... Losses are rarely the result of only a single pest or a stress acting in isolation thus, more attention needs to be paid to rice injury profiles that result from the collective effect of the many stresses acting upon a crop in a given season. Recent studies concluded that the degree of yield loss caused by a given insect pest complex in a field depends significantly not only on pest numbers but also on their interaction with the rice production environment (Willocquet et al. 2000). Some of the environmental factors may increase crop compensation, others may exacerbate loss, while others may be neutral. ...
... Johnson (1987), in a literature search of multiple pest interactions that included plant diseases, weeds, and insects, noted 57% of relationships on a variety of crops were antagonistic with the balance being evenly divided among additive and synergistic. Pinnschmidt et al. (1995) and Willocquet et al. (2000), using computer simulations, concluded that losses from multiple pest infestations from weeds, sheath blight, and stemborers usually exhibited antagonistic responses, even when the same combinations were tested at twice the infestation levels. Our field trial in Guimba, on the other hand, showed synergistic relationships from the same group of pests, possibly indicating that the crops were under greater stress from other causes as well as those manipulated in the trials. ...
... This may mean that there might have been an additive outcome if pest densities were greater. The fact that Willocquet et al. (2000) found doubling pest densities did not change the yield loss relationship, points to the need to identify the plant physiological pathways affected by pest combinations and understand how these affect loss. How is it that sheath blight in one experiment is associated with highest yields while in the next is responsible for synergistic losses? ...
Single and multiple species infestations of common insect pests were compared in terms of yield loss involving crop stresses that included weeds, sheath blight (Rhizoctonia solani Ku¨nh.), drought, and deficiencies of N and solar radiation in addition to insect pest infestations. Losses from the various combinations of stresses were found to be additive, antagonistic, or synergistic. Most combinations of insect pests produced additive losses, with the balance being antagonistic, so implying that compensation occurred. Combinations of insect damage with other biotic and abiotic stresses produced mostly synergistic crop losses, which were significantly greater than additive ones. Among insect pests, additive losses resulted from yellow stemborer (Scirpophaga incertulas [Walker]) attacking two rice growth stages while the same was true for leaffolder (Cnaphalocrocis medinalis [Gueneé]). Only the combination of whorl maggot (Hydrellia philippina Ferino) and defoliators (a mixture of Naranga aenescens Moore and Rivula atimeta [Swinhoe]) produced synergistic losses. Knowing the mode of yield loss from combinations of different plant stresses will aid the farmer in making decisions regarding which ones should be corrected and which the crop can tolerate.
... Losses are rarely the result of only a single pest or a stress acting in isolation thus, more attention needs to be paid to rice injury profiles that result from the collective effect of the many stresses acting upon a crop in a given season. Recent studies concluded that the degree of yield loss caused by a given insect pest complex in a field depends significantly not only on pest numbers but also on their interaction with the rice production environment (Willocquet et al. 2000). Some of the environmental factors may increase crop compensation, others may exacerbate loss, while others may be neutral. ...
... Johnson (1987), in a literature search of multiple pest interactions that included plant diseases, weeds, and insects, noted 57% of relationships on a variety of crops were antagonistic with the balance being evenly divided among additive and synergistic. Pinnschmidt et al. (1995) and Willocquet et al. (2000), using computer simulations, concluded that losses from multiple pest infestations from weeds, sheath blight, and stemborers usually exhibited antagonistic responses, even when the same combinations were tested at twice the infestation levels. Our field trial in Guimba, on the other hand, showed synergistic relationships from the same group of pests, possibly indicating that the crops were under greater stress from other causes as well as those manipulated in the trials. ...
... This may mean that there might have been an additive outcome if pest densities were greater. The fact that Willocquet et al. (2000) found doubling pest densities did not change the yield loss relationship, points to the need to identify the plant physiological pathways affected by pest combinations and understand how these affect loss. How is it that sheath blight in one experiment is associated with highest yields while in the next is responsible for synergistic losses? ...
Single and multiple species infestations of common insect pests were compared in terms of yield loss involving crop stresses that included weeds, sheath blight (Rhizoctonia solani Künh.), drought, and deficiencies of N and solar radiation in addition to insect pest infestations. Losses from the various combinations of stresses were found to be additive, antagonistic, or synergistic. Most combinations of insect pests produced additive losses, with the balance being antagonistic, so implying that compensation occurred. Combinations of insect damage with other biotic and abiotic stresses produced mostly synergistic crop losses, which were significantly greater than additive ones. Among insect pests, additive losses resulted from yellow stemborer (Scirpophaga incertulas [Walker]) attacking two rice growth stages while the same was true for leaffolder (Cnaphalocrocis medinalis [Guenée]). Only the combination of whorl maggot (Hydrellia philippina Ferino) and defoliators (a mixture of Naranga aenescens Moore and Rivula atimeta [Swinhoe]) produced synergistic losses. Knowing the mode of yield loss from combinations of different plant stresses will aid the farmer in making decisions regarding which ones should be corrected and which the crop can tolerate.
... Nowadays, with the ever-increasing human population, the need for efficient food production and effective crop protection has never been greater (Willocquet et al. 2000). In spite of our best efforts, food resources lost to insects either before or after harvest remain unacceptably high. ...
... The optimisation of pest control in an economically and ecologically sound manner, accomplished by the coordinated use of multiple tactics to assure stable crop production and to maintain pest damage below the economic injury level while minimising risks to man and the environment (Ehler 2006). Complete control of pests is neither necessary in most cases for maximum yields nor appropriate for IPM (Willocquet et al. 2000;). Nearly all crops can tolerate a certain amount of pest damage without appreciable effects on vigor and yield. ...
... Crop losses, in particular yield losses, have been studied hoping that the knowledge will provide quantitative estimates of the effect of insect pests (Tolman et al. 1986). Concurrently, scientists involved in making decisions on insect pests' management require information on the benefits accruing from a particular management action taken (Willocquet et al. 2000). Research leaders and agencies funding insect in comparison with other disciplines have used crop loss information as a basis for resource allocation and research prioritisation (Savary et al. 2000). ...
Levels of economic insect damage and their effects on crop production are the most
often-discussed issue in insect management today. The economic injury level (EIL)
concept is the base for decision-making in most integrated pest management (IPM)
programs. IPM programs are fundamentally different from control approaches
that handle insect problems by focusing on tolerating insect effects. EIL is essential
for IPM programs as it indicates which levels of insect populations can be tolerated
and which cannot. By increasing our ability to tolerate insects, it is possible to
eliminate or reduce the need for management tactics. Scientists can maintain
environmental quality through better decisions on the use of those tactics. EILs
help maintain environmental quality by reducing unnecessary use of management
tactics, especially insecticides. However, including environmental considerations
explicitly in the decision-making process could greatly improve the ability of IPM
to sustain environmental quality. The EIL components include economic damage,
economic thresholds, and the EIL itself. Increased availability of calculated EILs
and their related economic thresholds would reduce unnecessary use of management
tactics. An environmental EIL evaluates a management tactic based not only
on its direct costs and benefits to the user but also on its effects on the environment.
There are many factors that can reduce crop yield. One important cause is insects.
Insects that cause loss to the fruits are frequently more destructive than those that
damage leaves, stems and roots. For example, cotton is infested by Spodoptera
littoralis (Boisd.), Pectinophora gossypiella (Saund.), Helicoverpa armigera (Hu¨ n.)
and Earias insulana (Boisd.) cause the greatest yield losses. The amount of yield loss
is dependent upon a number of factors, i.e., plant variety, soil fertility, insect
population and skill in handling crop production, etc. Comparatively tolerant
varieties, even at the cost of slightly less yield potential, will be more suitable under
such conditions.
... Crop growth and development must however, reflect the combination of environmental factors, including the patterns of cropping practices, i.e. production situations where rice production takes place (De Wit and Penning de Vries, 1982;Savary, 1991;Rabbinge, 1993). A first step in this direction was made with the development of a preliminary yield loss simulation model (Willocquet et al., 1998(Willocquet et al., , 2000. This model is a simple crop growth simulator, which can simulate attainable growth and yield in varying production situations. ...
... The model structure is also detailed enough to accommodate a number of injury mechanisms, representing a wide range of rice pests. Preliminary results on the model structure and performances (Willocquet et al., 2000) indicated adequate simulation of attainable growth and yield in selected production situations, and of yield losses caused by a few pests. The preliminary model was thus expanded to a more detailed version, in order to simulate yield losses caused by an array of pests in a wide range of lowland rice production situations. ...
... RICEPEST consists of two inter-connected sub-models. The first submodel simulates the dynamics of the rice crop biomass, and the second simulates the dynamics of tiller population (Willocquet et al., 1998(Willocquet et al., , 2000. Damage mechanisms for injuries due to eight different rice pests were included in the model. ...
RICEPEST, a model simulating yield losses due to several rice pests (sheath blight, brown spot, sheath rot, bacterial leaf blight, stem borers, brown plant hopper, defoliating insects, and weeds) under a range of specific production situations of tropical Asia was developed. The model was assessed, using: (1) combined data sets generated by a series of test-experiments conducted in different sites of the Philippines, India, and China; and (2) one additional, independent, validation-experiment where a wide range of production situations and injury profiles were manipulated at a single site. Model evaluation was based on the analysis of two output variables: grain yield and relative yield loss. The paper reports results of qualitative and quantitative methods used to assess RICEPEST. Qualitative evaluation involved visual examination of graphs where deviations (simulated minus observed) are plotted against simulated values, and displaying an area of acceptance. This method showed that, in general, RICEPEST accounted well for the yield reducing effects of rice pests. Two areas for potential improvement of RICEPEST were however, identified: the simulation of damage caused by dead hearts in water-stressed environments, and the simulation of damage caused by weeds. Quantitative evaluations made use of equivalence- and χ2-tests. The equivalence tests rejected (P≤0.05) the hypothesis of difference between simulated and observed yield and relative yield loss larger than a preset tolerance in both test- and validation-experiments. Conversely, the χ2-tests did not reject the hypothesis of difference in categorised simulated and observed yields and relative yield losses (P≤0.05) in both test- and validation-experiments. RICEPEST proved to simulate adequately yield losses and can be used as a tool to set research priorities for rice crop protection in tropical Asia.
... In a previous study based on surveys done in farmers' rice fields in the region (49), production situations (4,13,36,47) for lowland rice were shown to be strongly associated with injury profiles, i.e., specific combinations of injuries that affect a rice crop during the course of a cropping season. This information generated a frame-work to develop RICEPEST, a simulation model for yield losses due to multiple injuries with a range of different production situations (65,66). RICEPEST was validated for an array of production situations and injury profiles (65) using a multiple-site network of independent field experiments in the Philippines (Central Luzon) (62), China (Zhejiang Province) (71), and India (Uttar Pradesh) (64). ...
... Simulations were performed with RICEPEST, a crop growth model that accounts for the effects of production situations on crop growth and the growth-reducing effects of pest injuries (65,66). The time step of the model is 1 day, and the system considered is 1 m 2 of a rice crop. ...
... Rice plots corresponding to production situation by injury profile combinations were monitored over the course of the season with respect to crop growth (dry weight of organs) and development and dynamics of injuries. This allowed comparisons of observed and simulated outputs for model validation (62,(64)(65)(66)71). ...
ABSTRACT A simulation study was conducted to assess the current and prospective efficiency of rice pest management and develop research priorities for lowland production situations in tropical Asia. Simulation modeling with the RICEPEST model provided the flexibility required to address varying production situations and diverse pest profiles (bacterial leaf blight, sheath blight, brown spot, leaf blast, neck blast, sheath rot, white heads, dead hearts, brown plant-hoppers, insect defoliators, and weeds). Operational definitions for management efficacy (injury reduction) and management efficiency (yield gain) were developed. This approach enabled the modeling of scenarios pertaining to different pest management strategies within the agroecological contexts of rice production and their associated pest injuries. Rice pests could be classified into two broad research priority-setting categories with respect to simulated yield losses and management efficiencies. One group, including weeds, sheath blight, and brown spot, consists of pests for which effective pest management tools need to be developed. The second group consists of leaf blast, neck blast, bacterial leaf blight, and brown plant-hoppers, for which the efficiency of current management methods is to be maintained. Simulated yield losses in future production situations indicated that a new type of rice plant with high-harvest index and high-biomass production ("New Plant Type") was more vulnerable to pests than hybrid rice. Simulations also indicated that the impact of deployment of host resistance (e.g., through genetic engineering) was much larger when targeted against sheath blight than when targeted against stem borers. Simulated yield losses for combinations of production situations and injury profiles that dominate current lowland rice production in tropical Asia ranged from 140 to 230 g m(-2). For these combinations, the simulated efficiency of current pest management methods, expressed in terms of relative yield gains, ranged from 0.38 to 0.74. Overall, the analyses indicated that 120 to 200 x 10(6) tons of grain yield are lost yearly to pests over the 87 x 10(6) ha of lowland rice in tropical Asia. This also amounts to the potential gain that future pest management strategies could achieve, if deployed.
... Dynamic process-based models of plant functioning provide a powerful framework for an assessment of these effects. Indeed, coupled pest-crop models can help to consider multiple pests and map out the pest disturbance pathways, as shown by modeling studies on rice (CERES-Rice: Pinnschmidt et al., 1995;RICEPEST: Willocquet et al., 2002, Willocquet et al., 2000, Willocquet et al., 1998, wheat (WHEATPEST: Willocquet et al., 2008) and potato (Johnson, 1992), as well as various annual crops (InfoCrop: Aggarwal et al., 2006), and briefly addressed on apple by Demestihas et al. (2019). The modeling approach of these studies relies on the concepts of disturbance mechanisms and coupling points (Donatelli et al., 2017;Kropff et al., 1995;Pinnschmidt et al., 1995). ...
... where PAR and ABS are the photosynthetically-active and absorbed radiation received by the PU's leafy shoots in the absence of pests (μmol photon m − 2 s − 1 ), and rfRAD mold is the reduction factor of received radiation by sooty mold (dimensionless). Multiplying a target variable by a reduction factor (ranging from 0 to 1) corresponds to a principle proposed by Pinnschmidt et al. (1995), which has already been put into practice in RICEPEST (Willocquet et al., 2002;Willocquet et al., 2000;Willocquet et al., 1998) and WHEATPEST (Willocquet et al., 2008). The reduction factor rfRAD mold was obtained by extending the equation describing the disturbance from the leafy shoot to the PU scale by the use of r RAA , the proportion of RAA-infested leafy shoots per PU: the part of the equation on the left of the plus sign refers to PU-infested shoots where the reduction of received radiation is (1 − rad mold × area mold ) and the part on the right to healthy shoots, where there is no reduction. ...
CONTEXT. In agroecosystems, crop pests have long been controlled by the intensive use of synthetic pesticides. To lower the dependency on pesticides, alternative crop protection strategies such as Integrated Pest Management (IPM) and Agroecological Crop Protection (ACP) have been proposed. In this context, it is crucial to consider multiple pests and their effects on host plant functioning and, ultimately, on system performance. OBJECTIVE. We aim to develop a pest-crop model to account for the effect of multiple pests on fruit tree functioning and the resulting ecosystem services provided by an orchard, using apple as a case study.
METHODS. First, we identified possible mechanisms of pest disturbance on apple tree for ten major pests: rosy and woolly apple aphid, codling, oriental and tortrix moths, red mite, powdery mildew, European canker, fire blight and apple scab. We classified them into seven functional groups: resource stealers, leaf area reducers, assimilation rate reducers, water transport reducers, assimilate sappers, fruit senescence accelerators and fruit marketability depreciators. Second, we defined coupling functions for each functional group. These describe the reduction of target variables in an existing fruit tree model (QualiTree), i.e. the variables to which the pest disturbances directly apply, as a function of pest abundances. Parameter values of the coupling functions were estimated based on the literature. Third, we used simulations in standard cultural conditions and a Monte Carlo Bayesian Sensitivity Analysis to test and hierarchize the effect of pests on the variation of nine indicators of four classes of ecosystem services (namely fruit production, climate regulation, soil nitrogen availability and water cycle maintenance and regulation). RESULTS AND CONCLUSIONS. Only four indicators were affected by the considered pests: refractometric index (measuring fruit sugar content), marketable yield, carbon sequestration and water drainage. The variations in refractometric index and in marketable yield were largely explained by the main effects of water transport reducers and fruit senescence accelerators, respectively. The latter also contributed the most to carbon sequestration variation. The variation in water drainage was largely explained by the main effect of mildew, an assimilation rate reducer. SIGNIFICANCE. Our approach provides an operational tool to assess the impact of multiple pests on orchard ecosystem services. The model also opens up the possibility of studying the impact of pests in different ecological and technical contexts. For this, it must be coupled with models capable of predicting pest abundances as a function of cultivation practices and biotic and abiotic conditions.
... Of the field data-based simulation models that have been developed, most of these do not consider full loop feedbacks between network components and population dynamics (Tonnang et al., 2017). For instance, single-species models and decision support systems give valuable information to decision-makers (Andrade-Piedra et al., 2005;Horton et al., 2002;Klueken et al., 2009;Ould-Sidi and Lescourret, 2011;Willocquet et al., 2002Willocquet et al., , 2000, but are limited to external driving factors that impact the pest directly (e.g., pesticides or weather) and not through ecological pathways; as such, they cannot consider potential unexpected, indirect impacts of the driving factors on the pest through interactions with other species components of the ecological network. A notable exception is the GETLAUS01 model, which includes a model of aphid populations along with dynamics for several predators (Gosselke et al., 2001). ...
... Purely cropping models such as DSSAT, APSIM, STICS and CropSyst (Brisson et al., 2003;Jones et al., 2003;Keating et al., 2003;Stöckle et al., 2003) allow for the informed design of agricultural systems' cropping choices, soil use, and fertiliser and irrigation regimes, and may even allow users to estimate (and respond to) crop damage from externally supplied pest populations. Singletrophic level models (Andrade-Piedra et al., 2005;Donatelli et al., 2017;Horton et al., 2002;Klueken et al., 2009;Ould-Sidi and Lescourret, 2011;Willocquet et al., 2002Willocquet et al., , 2000, on the other hand, can give immediate management recommendations (e.g., when the pest risks becoming high enough to cause economic crop damage, and when corrective action should be taken), while qualitative models such as that of Pocock et al. (2012) offer very interesting theoretical insights but little in terms of practical crop management recommendations. However, none of these approaches in isolation can explain how other trophic components (e.g., predators and parasitoids) allowed or caused pest levels to rise to unacceptable levels in the first place. ...
Agroecological food web modelling, which models trophic relationships between the species present in agricultural fields, has significant potential uses in the fields of theoretical and practical agroecology, specifically regarding the planning and evaluation of integrated pest management and biocontrol strategies. However, the practical use of such models with field data has been hindered by their complexity and difficulty in calibration, as well as the lack of software tools to streamline and standardise their development and evaluation. In this research we present a new modular software tool, Tiko’n, which allows users to rapidly (within a few lines of Python code) develop, calibrate and validate agroecological food web population dynamics models based on observed field population data. We then demonstrate its use in developing a food web model of a coconut pest in Sri Lanka and apply the model to assessing the potential impact of alternative biocontrol strategies. The software allowed for very rapid model development and adjustment and showed that smaller parasitoid releases may be more efficient than a single, larger application. It is the authors’ hope that this tool will contribute to a more widespread use of agroecological food web modelling in research and agronomic practice.
... We report here (Rabbinge and Vereijken, 1980;Boote et al., 1983;Kropff et al., 1995), that is, which simulates the physiological effects of injury on crop growth and yield. The general structure of WHEATPEST is derived from RICEPEST, a model developed for rice yield loss analysis (Willocquet et al., 2000(Willocquet et al., , 2002(Willocquet et al., , 2004, and from a model developed by Johnson (1992) for potato multiple pests. The acronyms used in the model are listed in Table 1, and the model structure is shown in Fig. 1. ...
... Weeds affect wheat growth through competition for light, nutrients, and water (Spitters, 1989). The overall effect of weeds on wheat physiology can be reflected as a reduction factor of RUE (RF WEED ), which depends on weed biomass (Willocquet et al., 2000). We used in WHEATPEST the same parameter as in RICEPEST ...
It is necessary to diagnose damages caused by multiple pests in order to design efficient cropping systems less dependent on pesticides. These diagnoses should quantify and classify hierarchically yield losses caused by various pests (pathogens, weeds, and insects) in order to help design crop management plans, or cropping systems, less vulnerable to the most detrimental pests. However, there is a lack of data on the incidence, severity and damages of various pests in European commercial fields. In order to address this issue, WHEATPEST, a production situation-based simulation model for wheat, was developed to incorporate drivers of variable production situations and their related injury profiles. It is a simple mechanistic agrophysiological model which incorporates damage mechanisms to simulate the physiological effects of several injuries (caused by pathogens, pests, and weeds) on crop growth and yield. Model inputs consist of weather data (daily temperature and radiation) and drivers for production situation and for injury profiles. Model outputs are series of dynamic variables over time: development stage, dry biomass of organs, Leaf Area Index, and final yield. Simulation drivers were derived from published reports, in particular through a meta-analysis of highly detailed farmers' field surveys in the United Kingdom and the Netherlands. Preliminary analysis of the model's performances indicates that WHEATPEST conforms with available published reports in a range of production situations and injury profiles. This work highlights the need for the collection of standardised data on both production systems characteristics and multiple pest injuries at the European scale, and the usefulness of modeling tools for basic research and policy. It is necessary to diagnose damages caused by multiple pests in order to design efficient cropping systems less dependent on pesticides. These diagnoses should quantify and classify hierarchically yield losses caused by various pests (pathogens, weeds, and insects). An injury profile is the combination of injury levels caused by multiple pests (pathogens, insects, weeds) that affect a crop during a growing cycle (Savary et al., 2006). Production situations, which can be defined as the bio- physical and socio-economic environments under which a crop is grown, and injury profiles are strongly linked (Zadoks, 1984). However, there is a lack of data on the injury profiles associated with various production situations at the European level, especially for wheat, the most heavily cultivated crop in Europe. In order to help address this issue, a mechanistic simulation model, WHEATPEST (Willocquet et al., 2008), was developed to simulate the harmful effects of pathogens, pests, and weeds in a simple, open, generic manner. This paper briefly presents the overall structure of WHEATPEST and simulation results.
... We report here (Rabbinge and Vereijken, 1980;Boote et al., 1983;Kropff et al., 1995), that is, which simulates the physiological effects of injury on crop growth and yield. The general structure of WHEATPEST is derived from RICEPEST, a model developed for rice yield loss analysis (Willocquet et al., 2000(Willocquet et al., , 2002(Willocquet et al., , 2004, and from a model developed by Johnson (1992) for potato multiple pests. The acronyms used in the model are listed in Table 1, and the model structure is shown in Fig. 1. ...
... Weeds affect wheat growth through competition for light, nutrients, and water (Spitters, 1989). The overall effect of weeds on wheat physiology can be reflected as a reduction factor of RUE (RF WEED ), which depends on weed biomass (Willocquet et al., 2000). We used in WHEATPEST the same parameter as in RICEPEST ...
The production situation–injury profile paradigm can be used as a framework to assess the harmfulness of multiple-pest complexes in a changing agriculture. A mechanistic simulation model for wheat, WHEATPEST, was developed to incorporate drivers of (i) variable production situations and (ii) their related injury profiles. The model simulates the harmful effects of pathogens, pests, and weeds in a simple, open, generic manner. Simulation drivers were derived from published reports, in particular through a meta-analysis of highly detailed farmers’ field surveys in the United Kingdom and the Netherlands. Preliminary analysis of the model's performances indicates that WHEATPEST conforms with available published reports in a range of production situations and injury profiles. While improvement on some components of the model are discussed, this work points at the need for the collection of cross-disciplinary, reasonably accurate, and standardised data at a system's level, and at the usefulness of modelling tools for basic research and policy.
... An individual crop stand is usually not exposed to a single organism but rather multiple harmful organisms. Type 4 knowledge considers the effects of several harmful organisms (pathogens, pests, weeds) using experimentalempirical (20,47,50,53,75,(113)(114)(115)133) or mechanistic-simulation approaches (49,52,96,138,139), or both. Because of its integrating nature and its scope over a range of harmful organisms, type 4 knowledge is particularly relevant to D-decisions as well as P-decisions. ...
... RICEPEST, a simulation model of rice yield losses, enables the simultaneous handling of production situations and injury profiles ('drivers'), as well as the modeling of management options and strategies (138)(139)(140). Overall, simulations indicate that 120 to 200 × 10 6 tons of grain yield are lost yearly to pests over the 87 × 10 6 ha of lowland rice in tropical Asia. ...
This review considers the cascade of events that link injuries caused by plant pathogens on crop stands to possible (quantitative and qualitative) crop losses (damage), and to the resulting economic losses. To date, much research has focused on injury control to prevent this cascade of events from occurring. However, this cascade involves a complex succession of components and processes whereby knowledge on crop loss generates entry points for management. Proposed here is a framework linking different types of knowledge on crop loss to a range of decision categories, from tactical to strategic short- or long-term. Important advances in this field are now under way, including a probabilistic treatment of the injury-damage relationship, or analyses of the sources of uncertainty attached to some components of the decision process. Management of injury profiles, rather than individual injuries, and shifts in dimensionality of crop losses are anticipated to contribute to the design of sustainable agricultural systems, and address global issues concerning food security and food safety.
... The effect of plant disease on plant physiology and, consequently, on yield can be incorporated as damage mechanisms into mechanistic simulation models in order to model the partitioning of carbohydrates (Willocquet et al., 2000(Willocquet et al., , 2002. However, prior to the development of simulation models that take damage mechanisms into account, simulation of healthy host growth is needed. ...
Grapevine leaf rust (GLR) and soybean rust (SBR), caused by Neophysopella tropicalis and Phakopsora pachyrhizi , respectively, may lead to early defoliation of the host, depending on disease severity level. The rate of defoliation is an important parameter in mechanistic models aimed at simulating yield loss, but such knowledge is not available for these rust diseases. This work aimed to (i) relate the temporal dynamics of GLR and SBR to defoliation; and (ii) estimate the relative rates of defoliation and model their relationship with rust severities. Grapevine and soybean plants were inoculated in the field at increasing concentrations of urediniospore suspensions of the respective causal agent. Control plots of the vineyard and the soybean field were protected with sequential fungicide sprays to evaluate natural defoliation. Rust severity (proportion of area affected) of each leaf or leaflet was evaluated every three or four days on 1323 grapevine leaves and 655 soybean leaflets, respectively. The relative rates of defoliation were estimated as the slope parameters of linear regression of the Napierian logarithm of the number of alive leaves or leaflets over time. Defoliation rates in grapevine and soybean incremented logarithmically with the increase of rust severity. Defoliation rates on symptomless grapevine and soybean leaves were 0.018 and 0.05 day ⁻¹ , respectively, while they averaged 0.033 day ⁻¹ on diseased grapevine leaves (rust severity between 5% and 12%), and 0.12 day ⁻¹ on diseased soybean leaflets (rust severity between 25% and 60%). Thus, a quantitative relationship was established between rust severity and defoliation on grapevine and soybean.
... In fact, the regular use of chemicals produces the resistance in plant pathogens and has created hazardous effects on the environment & ecosystem [20]. which enhances the urge in peoples for finding the safe management strategy against rice disease [21]. In such circumstances, use of plant hormones have an important role to minimize the severity and incidence of brown leaf spot [22]. ...
The plant hormones application increased the resistance in rice plants against brown leaf spot disease. That’s, why, the effect of five plant hormones (salicylic acid, citric Acid, K2HPO4, KH2PO4, benzoic acid) on the resistance of rice plants (Oryzae sativa L.) to brown leaf spot disease cause by Bipolaris oryzae were evaluated in field. The results showed that all of the plant hormones evaluated distinctly increased resistance of rice to BLS disease. All plant hormones employed at three concentrations (0.5, 0.75 and 1%), significantly decreased the incidence of brown leaf spot. Salicylic Acid exhibited minimum disease incidence (%) as compared to benzoic acid, K2HPO4, KH2PO4, and citric acid under greenhouse and field condition as compared to control. Interaction between treatments and concentration (T×C) expressed that salicylic acid expressed minimum disease incidence at 0.05% (22.52), 0.75% (19.92) and 1.0% (17.32) %, followed by benzoic acid (27.26,25.23,22.92), K2HPO4 (28.78,25.72,22.83), KH2PO4 (32.61,29.94,27.62) and citric acid (36.96,34.27,31.55) % respectively in field condition. These results recommended that plant hormones have key role in controlling the brown leaf spot disease through inducing resistance in rice plants.
... The model used in this study, named POTATOPEST, is an adapted version of WHEATPEST and RICEPEST, two mechanistic, process-based models originally developed for wheat (Willocquet et al., 2008) and rice (Willocquet et al., 2004(Willocquet et al., , 2002(Willocquet et al., , 2000, respectively. Like its predecessors, POTATOPEST is a simplified simulation model in which only the main agro-physiological processes that define the development of the potato plant are taken into account (such as radiation interception, canopy development, tuber initiation and bulking, etc.), leaving out other processes that contribute little to the behaviour of the system (such as stem population density or canopy architecture) (Savary et al., 2018). ...
Modelling crop yield loss due to pathogens remains a challenge due to the complex and dynamic interactions involved. Here, we develop a parsimonious dynamic yield loss simulation model that couples a simple epidemiological model of late blight disease with a basic model of potato crop growth. The model is parameterized using 17 years of field data on disease progress of potato late blight and potato yield in southern Sweden. Simulated actual yields (yields obtained under the presence of Phytopthora infestans) correlated well (R2 = 0.66) with the actual yields observed in field trials, using a few widely accessible meteorological variables. The results also underline the impact of disease growth rate and the time of infection on yield losses, with relatively low rates and early onset of the epidemic producing large yield reductions. In addition, the importance of increasing temperature due to climate change on both potato crop and Phytophthora infestans development was simulated, suggesting that higher temperatures may be more detrimental for the pathogen than for the crop. Additional ways to expand the model are discussed, and the inclusion of other diseases and pests are encouraged, indicating the potential to improve disease management in potato production.
... Differently from abiotic risks, applied modelling of crop diseases and pests has been dominated by short term, tactical questions, such as the development of support capabilities to schedule scouting or pesticide applications (Donatelli et al. [20]). Consequently, there is a need to design protocols which can guide the collection of the experimental data needed to calibrate and evaluate crop loss models, including both epidemiological and crop data (Willocquet et al. [21]). This does not allow for the development of market product (insurance). ...
This paper aims at understanding what affects farmers’ choices to buy insurance against biotic threats. Using a survey-based dataset with 1187 observations on Veneto wine grapes farms, we regress a probit model with endogenous variables with a maximum likelihood (ML) routine. The results corroborate the microeconomic theory according to which risk-adverse individuals are more propense to insure. In our framework, the farmers’ socioeconomic characteristics are treated as endogenous variables, which exist/are predetermined before the choice to insure (or not). This paper discusses the results in a policy perspective.
... Consequently, the disease model validation was limited across diversified environments (Willocquet et al. 2004;Willocquet et al. 2002). Hence, the development of designs, guides, templates, and protocols is needed to collect the adequate and required standard data to validate the disease models effectively (Willocquet et al. 2000). Detailed observations should include the disease or pest data (Disease severity, incidence, injury level), weather data (temperature, humidity, and leaf wetness), and crop data (physiological processes such as respiration, photosynthesis, senescence, etc.) (Esker et al. 2012;Savary et al. 2006). ...
Biotic stress is one of the major environmental factors that affect the plant’s growth and life cycle. Plant pathogens are major constraints and severe threats to agricultural production in changing climate scenarios. The effects of climate variability on plant diseases and pathogens have been examined in various plant pathosystems. Climate change is predicted to affect the development of pathogens, their survival, vigor, sporulation, multiplicity, and host susceptibility that ultimately cause changes in the crop diseases. It also affects the inoculum dispersion and pathogenicity. These effects vary depending on pathosystems and geographic locations. Climate change not only affects optimal conditions of infection but also host specificity and infection mechanism in plants. Temperature, light, and humidity are the major factors that control the development and growth of diseases. So, climate change is an emerging challenge that is impacting and driving the plants and pathogens growth, disease development in a pathosystem. This overview is aimed to summarize the previous research, reviews, opinions, and recent trends in studying the effects of climate variability on pathogens and plants health. However, managing and predicting climate change impacts are complicated because of the interaction between the indirect effects and global climate change drivers. Similarly, uncertainty in plant disease development models in changing climate needs the diversification in management strategies. Protection of plants against diseases and pathogens is an essential direction for researchers to make the plants more resistant to pests and diseases. There is a need for further research in different areas under multiple climate-changing factors and scenarios using the disease modeling frameworks such as BIOMA and APSIM-DYMEX.
... The yield lost per individual infection is therefore 1.7 ×10 −4 g/lesion for necrotrophic epidemics (N) and 7.5 ×10 −4 g/lesion for biotrophic epidemics (B). Simulation thus indicates that infection by a biotrophic pathogen B causes yield losses that are 4.5 times greater than those caused by infection by a necrotrophic pathogen N. Quite a few such models have been developed, with examples including peanut rust (106), wheat aphids (101), potato late blight (103,137), sugar-beet viruses (103), and rice blast (19,65) as well as those for pathogens, weeds, and pests in rice (85,144,146,147) and wheat (143,145). These models provide invaluable quantitative insights into the processes leading to yield losses and thus guide strategic decisions, for example in plant breeding for disease resistance (137). ...
Plant pathology must contribute to improving food security in a safe operating space, which is shrinking as a result of declining natural resources, climate change, and the growing world population. This review analyzes the position of plant pathology in a nexus of relationships, which is mapped and where the coupled dynamics of crop growth, disease, and yield losses are modeled. We derive a hierarchy of pathogens, whereby pathogens reducing radiation interception (RI), radiation use efficiency (RUE), and harvest index increasingly impact crop yields in the approximate proportions: 1:4.5:4,700. Since the dawn of agriculture, plant breeding has targeted the harvest index as a main objective for domesticated plants. Surprisingly, the literature suggests that pathogens that reduce yields by directly damaging harvestable plant tissues have received much less attention than those that reduce RI or RUE. Ecological disease management needs to target diverse production situations and therefore must consider variation in attainable yields; this can be achieved through the reengineering of agrosystems to incorporate built-in dynamic diversity of genes, plants, and crop stands.
Expected final online publication date for the Annual Review of Phytopathology, Volume 58 is August 25, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... Several other variables representing disease progress (epidemics) such as time to symptom appearance, final disease severity, or rate of disease development can be used to model disease-losses relationships. For practical purposes, crop loss models are sometimes incorporated in crop growth models (Willocquet et al. 2000(Willocquet et al. , 2008. This approach generates explanatory models, which are expected to have a greater predictive value than descriptive models. ...
Understanding how populations of microbial pathogens and arthropod pests develop over time is critical for timely and effective intervention to control disease epidemics and pest infestations in agricultural production systems. Various elements including the pathogen or pest, host plant, natural enemies or competitors, environment, and human activity interact in complex ways, and some of these elements can be factored into mathematical models for pest population increase and disease progress. Greenhouse production affords a level of control over climate and growth environment, as well as the opportunity to release biological control agents, and thus the potential to influence pathogen and arthropod pest populations and their development to a much greater degree than in field production. To this end, thresholds for intervention must be derived based on the relationship between losses and yields weighed against the cost of intervention. In the context of integrated pest management, monitoring of pathogen and pest populations, as well as of the environment and the development of resistance to chemical pesticides such as fungicides and insecticides, is necessary to estimate the risk to the crop posed by these diseases and pests and to select the optimal method for their control.
... In areas where crop protection methods have not yet substantially developed or prove inefficient under rapid change of crop genotypes or cropping conditions, the share of pest, diseases and weeds in the responsibility for crop losses can be very large. For mechanistic crop models, impact mechanisms have been structured (Boote et al., 1983;Rabbinge and Rijsdijk, 1981;Savary et al., 2018) and exemplarily implemented (Luo et al., 1997;Pinnschmidt et al., 1995), but also more compact solutions have been presented (Savary et al., 2015;Willocquet et al., 2000). ...
... While reductions in RI are caused by leaf necrosis, reduction in plant stand, and/or by early leaf senescence, pathogens can affect RUE by reducing CO 2 uptake in the asymptomatic green leaf area, reducing plant turgor, or causing assimilative sapper (Boote et al. 1983;Johnson 1987). Information about the damage induced by pathogens is necessary for simulation models that quantitatively integrate the interac tion b etween disea se a nd c rop g rowth (Willocquet et al. 2000(Willocquet et al. , 2008Savary and Willocquet 2014). For instance, the model Genecrop-P has been developed to simulate grapevine growth over the years as well as to allow analysis of how diseases, such as downy mildew, affect physiological processes and yield of grapevine plants (Nogueira Júnior et al. 2018). ...
Downy mildew caused by Plasmopara viticola is the most destructive disease of grapevines. However, the mechanisms of damage caused by P. viticola to photosynthesis in Vitis labrusca remain unknown. Our aim was to quantify the effects of downy mildew on leaf gas exchange in V. labrusca, estimating virtual lesions (regions surrounding lesions where photosynthesis is null) and evaluating limitations imposed on photosynthesis. Grapevine plants cv. Niagara Rosada were inoculated with varying sporangia concentrations of P. viticola to obtain a range of disease severities. Leaf gas exchange and photochemical activity were measured in both diseased and healthy leaves and the eq. Y = (1 - x)β was used to correlate photosynthetic variables (Y) and disease severity (x), with β representing the ratio between virtual and visual lesions. Our results revealed a β-value of 2.79, indicating moderate virtual lesions. Photosynthetic rates decreased by about 83% in leaves with 50% of downy mildew severity and there were significant impairments in the maximum Rubisco carboxylation rate, maximum rate of electron transport driving regeneration of ribulose-1,5-bisphosphate, effective quantum efficiency of photosystem II and mesophyll conductance, even in plants showing less than 10% of downy mildew severity. In conclusion, our results not only reveal how P. viticola affects photosynthesis in V. labrusca, but also give insights into how crop modelling would be improved by taking into account virtual lesions and photosynthetic damage. Such key information is needed for simulation models that quantitatively integrate the interaction between disease and crop growth.
... attainable growth and yield in a range of production situations (e.g. Willocquet et al., 2000Willocquet et al., , 2002Willocquet et al., , 2004. ...
Yield loss analysis is critical to inform tactical and strategic decisions in crop health management, and requires quantification of three elements: the levels of injury caused by disease or pest, the actual (injured) yield, and the attainable (un‐injured) yield. Reverse modelling allows reconstructing an object or a process from limited information combined with a mathematical model. We apply this approach to estimate yield losses caused by diseases in winter wheat using a process‐based simulation model (WHEATPEST), in combination with field data generated by a network of experiments across France, where multiple disease injuries and actual yields, but not attainable yields, were measured. The analysis covers 70 [Year x Region x Variety x Crop Management] combinations encompassing (1) five years (2003 to 2008), (2) four French Regions, (3) two winter wheat varieties (one high yielding and one hardy variety), and (4) two levels of crop management corresponding to two levels of chemical intensification. The analysis involved three main successive simulation modelling steps: (1) of actual yield, (2) of attainable yield, and (3) of yield losses associated with individual diseases. Overall, simulated yield losses to combined diseases ranged from 0 to 4.2 t ha⁻¹, and averaged 0.80 t ha⁻¹. Septoria tritici blotch caused the highest mean yield loss of 0.66 t ha⁻¹. The results highlight the contribution of varietal improvement to agricultural sustainability and performances. Reverse modelling can be applied to other crops and diseases or pests, in order to estimate individual and combined disease yield losses.
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... Simplified agrophysiological models accounting for damage mechanisms were developed for rice (RICEPEST; Willocquet et al., 2000Willocquet et al., , 2002Willocquet et al., , 2004) and wheat (WHEATPEST; Willocquet et al., 2008). Both models consider photosynthesis (using Monteith's framework; Monteith 1972Monteith , 1977, assimilates partitioning towards plant organs, translocation of starch from stems to storage organs, and leaf senescence. ...
This article addresses the modelling of crop health and its impact on crop losses, with a special emphasis on plant diseases. Plant disease epidemiological models have many different shapes. We propose a summary of modelling structures for plant disease epidemics, which stem from the concepts of infection rate, of site, of basic infection rate corrected for removals (Rc), and of basic reproductive number (R0). Crop losses, the quantitative and qualitative impacts of diseases and pests on crop performances, can be expressed along many different dimensions. We focus on yield loss, defined as the difference between the attainable yield and the actual yield, in a production situation. The modelling of yield loss stems from the concept of damage mechanism, which can be applied to the wide range of organisms (including pathogens, weeds, arthropods, or nematodes) that may negatively affect crop growth and performances. Damage mechanisms are incorporated in crop growth models to simulate yield losses. In both fields, epidemiology and crop loss, we discuss the process of model development, including model simplification. We emphasize model simplification as a main avenue towards model genericity. This is especially relevant to enable addressing the diversity of crop pathogens and pests. We also discuss the usefulness of considering differing evaluation criteria depending on the stage of model development, and thus, depending on modelling objectives. We illustrate progress made on two global key crops where model simplification has been critical; rice and wheat. Modelling pests and diseases, and of the yield losses they cause on these two crops, lead us to propose the concept of crop health syndrome as a set of injury functions, each representing the dynamics of an injury (such as, for example, the time-course of an epidemic). Crop health in a given context can be represented by the set of such injury functions, which in turn can be used as drivers for crop loss models.
... Willocquet et al., 2000, Rahmani et al., 2015Deihimfard et al., 2015Zarakani et al., 2014 از P (mg.kg -1 ) ( پتاسیم میلي - بر گرم کیلوگرم ) K (mg.kg -1 ) اسديته خاک PH نیتروژن )%( N (%) آلي کربن )%( Organic Carbon (%) شن )%( Sand (%) سیلت )%( Silt (%) رس )%( ...
Introduction:
Crop growth simulation models are powerful tools in determining optimal agriculture management strategies and the sustainability of production in agroecosystems. These models predict plant growth, water use and yield to understand the response of crops to the dynamics of climate–plant–water systems, to evaluate physiological traits for genetic yield improvement and to help make decisions that optimize use of available resources. Since implementing field research required time and cost, thus computer simulation models can save time and money by simulation doing extensive testing. The CliPest model is a generic dynamic simulation model for evaluation of climate change impacts, crop yields and losses due to invasion multiple pests damage. Therefore, the objectives of the present study were: (1) to calibration of the CliPest model (2) to evaluate the performances CliPest model to simulating winter wheat growth, development and grain yield in different wild oat plant densities under Kermanshah weather condition.
Materials and Methods:
A field experiment was done based on randomized complete block design (RCBD) with four replications and a greenhouse experiment was conducted based on completely randomized design (CRD( with three replications to the CliPest model of calibration and validation in the campus of Agriculture and Natural Resources at Razi University during 2014-2015. The treatments were wild oat plant densities (0, 25, 50, 75 and 100 plant m-2) in the field experiment and nitrogen fertilizer application (3.1, 6.2 and 10.1 g urea pot-1) in the greenhouse experiment. The required model inputs were daily solar radiation (MJ.m-2.d-1), and daily minimum and maximum temperature (°C). Model performance was evaluated by comparing simulated and measured values of winter wheat phenological development stages, total dry weight and grain yield for independent wild oat plant densities treatments (fourth replication from the field experiment that did not use in the model calibration process) by root mean square error (RMSE), normalized RMSE (nRMSE) and index of agreement (d).
Results and Discussion:
The results of CliPest calibration showed that nRMSE for total dry weight yield and grain yield of winter wheat and total dry weight of wild oat observed 7.7, 3.1 and 23.4% , respectively. The results of CliPest validation showed that nRMSE for phonological development stages, total dry weight yield, grain yield winter wheat observed 2.4, 24.3, 4.8%, respectively and for phenological development stages and total dry weight of wild oat observed 2.2 and 23.4% of , respectively. The nRMSE for percent of total dry weight yield loss by wild oat damage and percent of grain yield loss due to wild oat damage in winter wheat observed 15.7 and 11.6%, respectively. The results of Clipest showed that with increasing of wild oat plant density, total dry weight and grain yield of winter wheat decreased which was agreement by obtained data in the field experiment. The results of index of agreement (d) and r2 coefficient between observed and simulated data compared to 1:1 line also showed that the CliPest was able to simulate successfully more than 90% and 95% of observed differences in studied traits, respectively.
Conclusion:
The results indicated that the CliPest model was able to simulate successfully the observed growth traits of winter wheat and wild oat as well as wheat yield loss by oat damage in different plant densities under Kermanshah climate condition. It seems that careful selection in calibrated parameters in the sensitivity analysis process, measure these parameters in the field and the greenhouse conditions and use of them in the model structure were the main reason to achieve high accuracy for predictions.
... Data on actual disease progress curves are necessary; but these only make sense in a context: accurate information on production situations (including crop varieties and cropping practices), together with weather data, is necessary, as well as quantitative information on crop growth, crop development, and yield. Chakraborty et al. (2000) pointed at the need for such data; this was repeated by Savary et al. (1996) and Willocquet et al. (2000Willocquet et al. ( , 2002 about rice health, and wheat (Willocquet et al. 2008). As a result, situations where such rich data sets on crop health, production situations, and crop performance are rare. ...
Scenario analysis constitutes a useful approach to synthesize knowledge and derive hypotheses in the case of complex systems which are documented with mainly qualitative or very diverse information. In this article, a framework for scenario analysis is designed and then, applied to global wheat health within a timeframe from today to 2050. Scenario analysis entails the choice of settings, the definition of scenarios of change, and the analysis of outcomes of these scenarios in the chosen settings. Three idealized agrosystems, representing a large fraction of the global diversity of wheat-based agrosystems, are considered, which represent the settings of the analysis. Several components of global changes are considered in their consequences on global wheat health: climate change and climate variability, nitrogen fertilizer use, tillage, crop rotation, pesticide use, and the deployment of host plant resistances. Each idealized agrosystem is associated with a scenario of change that considers first, a production situation and its dynamics, and second, the impacts of the evolving production situation on the evolution of crop health. Crop health is represented by six functional groups of wheat pathogens: the pathogens associated with fusarium head blight; biotrophic fungi; septoria-like fungi; necrotrophic fungi; soil borne pathogens; and insect-transmitted viruses. The analysis of scenario outcomes is conducted along a risk-analytical pattern, which involves risk probabilities represented by categorized probability levels of disease epidemics, and risk magnitudes represented by categorized levels of crop losses resulting from these levels of epidemics within each production situation. The results from this scenario analysis suggest an overall increase of risk probabilities and magnitudes in the three idealized agrosystems. Changes in risk probability or magnitude however vary with the agrosystem and the functional groups of pathogens. We discuss the effects of global changes on the six functional groups, in terms of their epidemiology and of the crop losses they cause. Scenario analysis enables qualitative analysis of complex systems, such as plant pathosystems that are evolving in response to global changes, including climate change and technology shifts. It also provides a useful framework for quantitative simulation modeling analysis for plant disease epidemiology.
... Willocquet et al., 2000, Rahmani et al., 2015Deihimfard et al., 2015Zarakani et al., 2014 از P (mg.kg -1 ) ( پتاسیم میلي - بر گرم کیلوگرم ) K (mg.kg -1 ) اسديته خاک PH نیتروژن )%( N (%) آلي کربن )%( Organic Carbon (%) شن )%( Sand (%) سیلت )%( Silt (%) رس )%( ...
Introduction: Crop growth simulation models are powerful tools in determining optimal agriculture management strategies and the sustainability of production in agroecosystems. These models predict plant growth, water use and yield to understand the response of crops to the dynamics of climate–plant–water systems, to evaluate physiological traits for genetic yield improvement and to help make decisions that optimize use of available resources (Soltani et al., 2006). Since implementing field research required time and cost, thus computer simulation models can be saves time and money by simulation doing extensive testing (Dastmalchi et al., 2012). The CliPest model is a generic dynamic simulation model for evaluation of climate change impacts, crop yields and losses due to invasion multiple pests damage (Mondani, 2012). Therefore, the objectives of the present study were: (1) to calibration of the CliPest model (2) to evaluate the performances CliPest model to simulating winter wheat growth, development and grain yield in different wild oat plant densities under Kermanshah weather condition.
Materials and Methods: A field experiment was done based on randomized complete block design with four replications and a greenhouse experiment was conducted based on completely randomized design with three replications to the CliPest model of calibration and validation in the campus of Agriculture and Natural Resources at Razi University during 2014-2015. The treatments were wild oat plant densities (0, 25, 50, 75 and 100 plant per m-2) in the field experiment and nitrogen fertilizer application (3.1, 6.2 and 10.1 g urea per pot) in the greenhouse experiment. The required model inputs were daily solar radiation (MJ.m-2. d-1), and daily minimum and maximum temperature (°C). Model performance was evaluated by comparing simulated and measured values of winter wheat phonological development stages, total dry weight and grain yield for independent wild oat plant densities treatments (the fourth replication from the field experiment that did not use in the model calibration process) by root mean square error (RMSE), normalized RMSE (nRMSE) and index of agreement (d).
Results and Discussion: The results of CliPest calibration showed that nRMSE for total dry weight yield and grain yield of winter wheat and total dry weight of wild oat were 7.7, 3.1 and 23.4% of observed averages, respectively. The results of CliPest validation showed that nRMSE for phonological development stages, total dry weight yield, grain yield winter wheat were 2.4, 24.3, 4.8% of observed averages, respectively and for phonological development stages and total dry weight of wild oat were 2.2 and 23.4% of observed averages, respectively. The nRMSE for percent of total dry weight yield loss due to wild oat damage and percent of grain yield loss due to wild oat damage in winter wheat were 15.7 and 11.6% of observed averages, respectively. The results of Clipest showed that with increasing of wild oat plant density, total dry weight and grain yield of winter wheat decreased which was agreement by observed data in the field experiment. The results of index of agreement (d) and r2 coefficient between observed and simulated data compared to 1:1 line also showed that the CliPest was able to simulate successfully more than 90% and 95% of observed differences in studied traits, respectively.
Conclusion: The results indicated that the CliPest model was capable to simulate successfully the observed growth traits winter wheat and wild oat as well as wheat yield loss due to weed damage in different plant densities under Kermanshah weather condition. It seems that careful selection in calibrated parameters in the sensitivity analysis process, measure these parameters in the field and the greenhouse conditions and use of them in the model structure were the main reason high accuracy for predict the observed data.
... Walker (1975) was one of the first to point out that yield loss from insect pests occurs in association with not only other kinds of insect pests, but other kinds of pests such as weeds, diseases, nematodes, etc., and concurrently from a multitude of abiotic factors such as nutrient deficiencies, moisture stress, low solar radiation, etc., where the contribution of each may or may not be additive. In other words, any factor that affects yield can also affect the degree of yield loss from insect pests (Bardner and Fletcher 1974; Willocquet et al. 2000). Poston et al. (1983) and Lamp et al. (1985) categorized the mode of interactions from multiple pest infestations as being: 1) additive (no interaction) where yield reductions caused by more than one pest attacking one plant are the sum of the reductions as if each species attacked separate plants, 2) synergistic where the response is significantly more than additive, and 3) antagonistic if the joint interaction is significantly less than additive. ...
We found that the combined attack of whorl maggot Hydrellia philippina Ferino and defoliators (mixed populations of green semilooper Naranga aenescens Moore and green hairy caterpillar Rivula atimeta [Swinhoe]) produced a synergistic yield loss on irrigated rice, while losses from stemborer Scirpophaga incertulas (Walker) and leaffolder Cnaphalocrocis medinalis (Guenée) were additive over both rice reproductive and ripening growth stages. Losses from insect pests became accentuated with each additional non-insect pest stress (weeds, low inorganic N, low solar radiation) added reaching a plateau with two or three such stresses. This result underscores the difficulty of predicting yield loss caused by a given insect pest complex in the field as it depends significantly not only on pests, but also on their interaction with the rice production environment. The combination of non-insect pest stress that produced the greatest yield loss was high solar radiation of the dry season, weed stress, and no added inorganic N. The null hypothesis was that the wet season with its low solar radiation would have caused the greatest stress and that the high solar radiation of the dry season would have allowed greater compensation to occur. The greatest non-insect stress for the most part turned out to be the combination of the dry season and weeds with N rate only contributing in some comparisons, and sometimes at 0 or 90 kg N/ha rates. We offer several explanations for this. The combination of the dry season and weeds was particularly stressful as the greater solar radiation benefitted weeds more than rice. Only rice suffered transplanting shock and in addition insect pests fed more on rice than weeds. At times the combination of dry season, no added N, and weed-free plots gave the lowest yield. In this case the rice crop suffered most from insect pest injury, transplanting shock, and N stress. That upredicable outcomes were probably due to the countervailing forces of compensation on the one hand and the effects of multiple stresses on the other. © 2014, Science and Technology Information Institute. All rights reserved.
... Walker (1975) was one of the first to point out that yield loss from insect pests occurs in association with not only other kinds of insect pests, but other kinds of pests such as weeds, diseases, nematodes, etc., and concurrently from a multitude of abiotic factors such as nutrient deficiencies, moisture stress, low solar radiation, etc., where the contribution of each may or may not be additive. In other words, any factor that affects yield can also affect the degree of yield loss from insect pests (Bardner and Fletcher 1974;Willocquet et al. 2000). Poston et al. (1983) and Lamp et al. (1985) categorized the mode of interactions from multiple pest infestations as being: 1) additive (no interaction) where yield reductions caused by more than one pest attacking one plant are the sum of the reductions as if each species attacked separate plants, 2) synergistic where the response is significantly more than additive, and 3) antagonistic if the joint interaction is significantly less than additive. ...
We found that the combined attack of whorl maggot Hydrellia philippina Ferino and defoliators (mixed populations of green semilooper Naranga aenescens Moore and green hairy caterpillar Rivula atimeta [Swinhoe]) produced a synergistic yield loss on irrigated rice, while losses from stemborer Scirpophaga incertulas (Walker) and leaffolder Cnaphalocrocis medinalis (Guenée) were additive over both rice reproductive and ripening growth stages. Losses from insect pests became accentuated with each additional non-insect pest stress (weeds, low inorganic N, low solar radiation) added reaching a plateau with two or three such stresses. This result underscores the difficulty of predicting yield loss caused by a given insect pest complex in the field as it depends significantly not only on pests, but also on their interaction with the rice production environment. The combination of non-insect pest stress that produced the greatest yield loss was high solar radiation of the dry season, weed stress, and no added inorganic N. The null hypothesis was that the wet season with its low solar radiation would have caused the greatest stress and that the high solar radiation of the dry season would have allowed greater compensation to occur. The greatest non-insect stress for the most part turned out to be the combination of the dry season and weeds with N rate only contributing in some comparisons, and sometimes at 0 or 90 kg N/ha rates. We offer several explanations for this. The combination of the dry season and weeds was particularly stressful as the greater solar radiation benefitted weeds more than rice. Only rice suffered transplanting shock and in addition insect pests fed more on rice than weeds. At times the combination of dry season, no added N, and weed-free plots gave the lowest yield. In this case the rice crop suffered most from insect pest injury, transplanting shock, and N stress. That upredicable outcomes were probably due to the countervailing forces of compensation on the one hand and the effects of multiple stresses on the other.
... The link between disease dynamics and yield reduction was mechanistically quantified through different models in the 2000s, but none of them addressed this link in grapevine. For example, some complex models on the yield effects of multiple pests and diseases, such as RICEPEST in rice (Willocquet et al., 2000(Willocquet et al., , 20022004) or WHEATPEST in wheat (Willocquet et al., 2008), described and simulated crop yield reductions that result from a certain level of disease severity and pest infestation, but the disease development that produces this severity has to be quantified by other means. Even if they were adapted to grapevine, these models would not allow us to simulate overall disease development, the effect of control strategies and the resulting yield loss. ...
... Collectively, these mechanisms amount to a reduction of radiation interception or to a reduction of radiation use efficiency by growing crop canopies (Waggoner and Berger 1987;Johnson 1987). As a result these mechanisms represent a basis for crop loss simulation modelling concepts (e.g., Teng and Gaunt 1980;Loomis and Adams 1983;Pace and Mackenzie 1987;Rouse 1988) and studies (e.g., Teng et al. 1977;Johnson and Teng 1990;Rossing 1991a;Johnson 1992;Pinnschmidt et al. 1995;Willocquet et al. 2000;. ...
The status of global food security is alarming, and protection against losses caused by
crop pests, plant diseases in particular, can play a critical role in improving food
security worldwide. The losses from pathogens are direct, as well as indirect; they have
a number of facets, some with short-, and others with long-term consequences. Most of
the agricultural research conducted in the 20th century focused on increasing crop
productivity. Plant pathology thus primarily focused on protecting crops from yield
losses. The challenge posed today is greater, however, because the food security
challenge is compounded by other essential issues, such as food safety and harvest
quality, combined with increasing limits to manage diseases due to shrinking natural
and human resources. As a research theme, the measurement, analysis, and
modelling of crop loss brings plant pathology to a disciplinary crossroad, involving
agrophysiology, agronomy, ecology, public health medicine, and economics, as well as
social sciences in general. It also brings plant pathology closer to other plant sciences
including plant-animal interactions, and weed sciences. Examples presented in this
article are crop losses in wheat in the USA, quality losses in cereals in Europe's
Northern countries, yield losses to diseases in rice in tropical Asia and yield losses in
wheat in France. This review underscores the existence of a wide and expanding array
of methods, which is available now for crop loss research. Many of these methods are
derived from Ecology and the Social Sciences, enabling the multifaceted nature of crop
losses to be addressed. We also conclude that too little is known of the physiology of
the diseased plant under yield-limiting conditions, of what is happening in farmers'
fields with respect to diseases and pests, crop management, and their relationships
with crop losses. Ground truth, documenting the current status of crop health in ranges
of production situations and the levels of direct (including impacts on quality of harvests
and toxin accumulation) and indirect (across economic and social fabrics) losses
through experiments specifically designed to measure crop loss, represent further
critical gaps.
... Compared to other diseases, ALS causes a strong defoliation. For example, for a disease severity of 10%, estimated RRDEF is 0.17 in ALS (this article), 0.035 in rice (Oryza sativa L.) blast (Bastiaans, 1993), 0.08 in rice sheath blight (Willocquet et al., 2000), and 0.024 in groundnut leafspot (Savary et al., 1990). ...
This work quantifies two important epidemiological features of the bean (Phaseolus vulgaris)/Phaeoisariopsis griseola pathosystem. The first is the effect of the number of nights of leaf wetness on infection efficiency. Infection efficiency was below 10% when inoculated leaflets were exposed to less than two nights of leaf wetness. Optimum infection efficiencies were obtained after three to four nights of leaf wetness, at about 50%. Further nights of leaf wetness did not increase the infection efficiency. The second feature quantified is the relative rate of leaflet defoliation for varying levels of angular leaf spot severity. It increased with disease severity according to a logarithm-like curve, and a relative rate of 0.23 day-1 was estimated for a severity of 18%. The implications of these results on the disease epidemiology are discussed.
... A further confounding effect in trying to associate losses with a single pest comes from synergistic losses from multiple pests, each at subeconomic densities, that attack jointly causing significantly higher losses than those caused by each acting singly (IRRI 1983(IRRI , 1984Wu et al. 1995). Furthermore the yield loss contribution of each pest is significantly influenced by associations of non-insect pest crop stresses (Savary et al. 1994Willocquet et al. 2000). ...
Action thresholds as decision tools for insecticide application were developed and tested against the major insect pests of rice at four sites in the Philippines over a 13-year period. Action threshold treatments were compared to the farmers' practice, prophylactic insecticide usage, and an untreated check. Yield loss data using the insecticide check method partitioned yield losses over three crop growth stages in the same test fields. Chronic pests that exceeded action thresholds in 79% of fields were whorl maggot Hydrellia philippina Ferino (Diptera: Ephydridae), defoliators Naranga aenescens Moore and Rivula atimeta (Swinhoe) (Lepidoptera: Noctuidae), leaffolders Cnaphalocrocis medinalis (Guenée) and Marasmia patnalis Bradley (Lepidoptera: Pyralidae), and stemborers Scirpophaga incertulas (Walker) and S. innotata (Walker) (Lepidoptera: Pyralidae). Minor chronic pests reached threshold levels in only one site each: rice bug Leptocorisa oratorius (F.) (Koronadal), whitebacked planthopper Sogatella furcifera (Horvath) (Zaragoza) and green leafhopper Nephotettix virescens (Distant) (Guimba); brown planthopper Nilaparvata lugens (Stål) did not exceed a threshold in any field. Stemborers were the most important pest group in terms of yield loss. Despite the insecticide check method underestimating losses, a mean crop loss of 0.62 t/ha was measured which showed ample scope for corrective action. But loss was evenly distributed across crop growth stages (0.15 – 0.24 t/ha) reducing the impact of insecticides. Action threshold treatments overall outyielded the untreated check, more so in the two sites with highest pest density. The benefit of thresholds was to reduce insecticide usage, as a cost saving. However all the practices showed poor economic returns including the farmers' practice. Farmers' practice employed low insecticide dosages and timing was not consistent with pest damage, but yields were often similar to threshold treatments. Farmers appear to use insecticide more for risk aversion than for profit. The best threshold characters when evaluated against resulting pest density and yield loss criteria showed accuracies > 90% correct decisions. Future work is needed to improve the insecticide response rather than monitoring tools. Thresholds need to be incorporated into improved crop management, which was often found suboptimal by farmers, to take advantage of the high levels of tolerance in modern high tillering cultivars. Crop husbandry practices which improve yield potential such as selection of longer maturing varieties and nitrogen fertilizer may be a more effective pest management strategy than insecticides.
... Savary et al. (1994) has attempted to take many of these into account with his crop loss assessment method that takes large samples of fields and monitors each frequently trying to quantify as many important variables as practical. They found Poston et al., 1983) low yields were the result of combinations of many of these factors that alone were subeconomic but in combination became economic (Savary et al., 1994;Willocquet et al., 2000). It is no wonder that rice yields are so variable even within a farm community as the crop can be affected by literally > 30 stresses each season, any one of which or of the multitude of combinations can affect yield. ...
As land area to expand rice production is limited most increases in crop production in Asia must come from increasing yields
on farms already under production. Insect pests are among the most important biological constraints limiting yield potential
of modern rices but the extent of damage depends on how vigorous the crop is growing as well as the number of biotic and abiotic
stresses affecting the crop that season. Yield loss data is useful for farmers, extension workers, researchers, and policy
makers. A number of methods to estimate losses are available and more than one should be used. As accurate crop loss assessments
are expensive to obtain for a nation on a regular basis, estimates are only available for limited areas of countries. Insect
pest resistance exists for epidemic pests but not for chronic pests, the insect pest group that causes greater losses every
year. Modern high tillering rices have greater capacity than traditional rices for compensation from insect pest damage and
that capacity is enhanced by agronomic practices thus integrated pest management should be thought of within the context of
crop management. For making better control decisions farmers need to assess the compensatory status of the crop and severity
of crop stress acting on it. Due to crop compensation capacity, farmers need not correct all stresses to obtain high yields
thus can combat the easiest or least expensive constraints and let the crop compensate for the rest. lf nations are to get
a handle on the extent of losses, farmers will need to be involved in the data gathering.
... More general models simulate a comprehensive pest complex on a given crop, e.g. rice with the model RICEPEST (Willocquet et al., 2000(Willocquet et al., , 2002. However, a lot of work on these aspects remains to be done. ...
To help agricultural advisors to propose innovative crop management systems, simulation models can be a complementary tool to field experiments and prototyping. Crop management systems can be modelled either by using a vector representing dates and quantities used as input parameters in crop models or by developing specific decision models linked with biophysical models. The general design process of crop management systems by simulation follows a four-step loop (GSEC): (i) generation; (ii) simulation; (iii) evaluation; (iv) comparison and choice. The Generation step can follow different approaches: from blind generation before simulation to optimization procedures using artificial intelligence algorithms during the loop process. Simulation is mainly an engineering problem. Evaluation process means assigning a vector of indicators to the simulated crop management systems. A three-point evaluation can be carried out on the simulated crop management systems: global, agronomic and analytical. Comparison and choice of different simulated crop management systems raise the question of “monetary” versus “non-monetary” comparison and how to aggregate different quantities such as drainage, nitrogen fertilisers, labour, etc. Different examples are given to illustrate the GSEC loop on the basis of research programs conducted in France. Methodological advances and challenges are then discussed.
... The yield-loss approach can thus be especially useful for ield-loss models have been used extensively. For example, Willocquet et al. (2000) build an extensive o say works for various pests and conditions and can be applied to ther crops. Their usefulness is also not restricted to the present conditions. ...
The expanding global economy presents various challenges to production and environmental systems worldwide. Biosecurity provides a framework for managing the risks presented by different types of diseases and species spread by globalisation. One element of biosecurity is protection against invasive alien species (IAS). These are species spread by human actions outside their natural zones of dispersal. IAS present a threat to biological diversity at all levels and may have a negative impact on the goods and services provided by ecosystems. IAS may result in non-production and production costs. The first category includes physical impacts materialising as environmental, health and cultural costs, whereas the second category includes the subsequent economic impacts, such as production losses, domestic market effects and trade effects. In addition, IAS may impose control costs either on the society or a specific sector, depending on the type of species and the chosen policy. Management of IAS is a public good and remains under-provided by the free market, which partly explains the involvement of the state in IAS control. A broad division of IAS management is between what is here called pre-emptive and reactive control. Pre-emptive control refers to actions taken to totally eradicate the IAS when found. Such actions reduce the probability of entry and/or establishment of IAS. Reactive control refers to letting a possible invasion to take place and be followed by application of reactive control measures, reducing the extent and magnitude of damages in the event of an invasion. Preventative actions are generally advocated as the preferred strategy to deal with IAS, but it is possible that the costs incurred due to an invasion are less than the costs incurred in continued preventative actions. In such a case, continued efforts to prevent the species from invading consume the limited resources and may lead to other, more dangerous, species not being targeted with sufficient
This chapter reviews approaches taken to assessing the impact of diseases on rice crops, primarily in tropical Asia, compared with insect pests and weeds. Published estimates of yield loss show great variation between and within reports. This reflects the diversity of metrics used and differences in method, location, scale, crop ecosystem etc., exposing the inconsistency of the estimates as representative of rice production. Results are then summarised of >1000 surveys of farmers’ fields conducted over 25 years to a standardised protocol. Estimates were made of the levels of injury to the growing crop attributed to each disease or pest species present, and of the crop yield. Records of physical, biological, and socio-economic characteristics showed the importance of the influence of the crop’s “production situation” on the impact of pathogen and pest species. These surveys were complemented by the results of yield-loss experiments, and by epidemic modelling (based on EPIRICE) and yield-loss modelling (based on RICEPEST). An overview of the results of this research programme is presented, together with a summary of recent trends in rice crop health in Asia. In contrast with views widely held in the 1980s, plant diseases now appear to have a much greater impact on rice production than insect pests.
This study provides a bio-economic assessment of the global climate suitability and probabilistic crop-loss estimates attributable to wheat leaf rust. We draw on a purpose-built, spatially-explicit, eco-climatic suitability model for wheat leaf rust to estimate that 94.4% of global wheat production is vulnerable to the disease. To reflect the spatio-temporal variation in leaf rust losses, we used a probabilistic approach to estimate a representative rust loss distribution based on long-term, state-level annual U.S. loss estimates. Applying variants of this representative loss distribution to selected wheat production areas in 15 epidemiological zones throughout the world, we project global annual average losses of 8.6 million metric tons of grain for the period 2000-2050 based on a conservative, base-line scenario, and 18.3 million metric tons based on a high-loss scenario; equivalent to economic losses ranging from US$1.5 to US$3.3 billion per year (2016 U.S. prices). Even the more conservative base-line estimate implies that a sustained, worldwide investment of US$50.5 million per year in leaf rust research is economically justified.
This chapter discusses the ecophysical background and management of the cropping systems of Asia, with emphasis on nutrition and diseases. There are several ecophysical, social, and economic factors that have played important roles in the development of the present cereal-based cropping systems (CBCS) of Asia. CBCS will continue to be important to meet increasing food demands in Asia. Even though the rice-wheat system will remain the most important sequence in South and Southeast Asia, the rice-maize system will expand in the future. In Central and West Asia, the wheat and barley-based cropping system will be predominant. These cereal–cereal cropping systems will have a sustainability challenge; hence, it will be important to integrate grain legumes into these cereal-based systems. Even though the CBCS in Asia are highly diverse due to contrasting ecophysical conditions in different regions, there are a few megasystems that account for a greater share of food production in the continent. These include cropping systems such as rice–rice in the tropical climate of East and Southeast Asia, rice–wheat in the intensive agro-ecosystems of China and South Asia, cereals–legumes in South Asia, and wheat–fallow and wheat–cotton in the dry lands of China, India, and West and Central Asia.
Rice is the most rapidly growing staple food in Africa and although rice production is steadily increasing, the consumption is still out-pacing the production. In Tanzania, two important diseases in rice production are leaf blast caused by Magnaporthe oryzae and bacterial leaf blight caused by Xanthomonas oryzae pv. oryzae. The objective of this study was to quantify rice yield losses due to these two important diseases under a changing climate. We found that bacterial leaf blight is predicted to increase causing greater losses than leaf blast in the future, with losses due to leaf blast declining. The results of this study indicate that the effects of climate change on plant disease can not only be expected to be uneven across diseases but also across geographies, as in some geographic areas losses increase but decrease in others for the same disease.
Crop growth models could stimulate growth and development based on science principles and mathematical equations. They also able to evaluate effects of climate, soil, water and agronomic management practices on crop yield. In the present study, an eco-physiological simulation model developed to assess wild oat damage to winter wheat growth and yield. The general structure of this model is derived from LINTUL1 model which modified to wild oat competition against winter wheat. LINTUL1 model was developed for simulation of spring wheat potential production level. In this study, first, we added development stage (DVS) and vernalization to LINTUL1 for simulation of winter wheat growth and development and then the model calibrated for potential production level. Finally, we incorporate harmful effects of wild oat to winter wheat growth and yield. Weather data used as input were average daily minimum and maximum temperature (°C) and daily global radiation (MJ m-2) in Mashhad, Iran. Parameter values were derived from the literature. The model is written in Fortran Simulation Translator (FST) programming language and then validated based on an experiment data. For these purposes different wild oat plant densities were arranged. The data of this experiment does not use for calibration. The results showed that this model was in general able to simulate the temporal changes in DVS of winter wheat and wild oat, total dry matter (TDM) of winter wheat and wild oat and yield loss of wheat due to wild oat competition in all treatments, satisfactorily. Root mean square error (RMSE) for winter wheat DVS, wild oat DVS, average winter wheat TDM, average wild oat TDM, and yield loss of winter wheat was 10.4, 14.5, 5.8, 7.6 and 7.5, respectively.
The future climate change will alter crop-pest interactions and will affect on amount of crops production, therefore, it is essential to predict pest damage in future conditions. Crop growth models are a technique that researchers could use to study possible impacts of climate change on crops. Although several dynamic models have been developed in recent years, most of them are not generally evaluating impacts of climate change on crop-pest interactions. In the present study, an eco-physiological crop model has been developed to meet these objectives. Its general structure relating to basic crop growth and yield is largely based on LINTUL model, and is written in Fortran Simulation Translator (FST) environment. In this modeling, first, we added phonological development stage (DVS) to LINTUL for simulation of winter wheat growth and then the model calibrated for potential production level. Finally, we incorporate effects of sunn pest damages to winter wheat growth and yield. Parameter values were derived from the literature. The model validated against observed values of DVS, total dry weight yield (TDWY) and grain yield (GY) of winter wheat and winter wheat yield loss due to sunn pest taken from a field experiment. Results indicated that the model was able to explain successfully the observed differences in DVS, TDWY and GY. The loss in TDWY and GY due to sunn pest was also explained satisfactorily. Our simulation results showed that life cycle of winter wheat (from 240 to 217) will be shortened due to accelerate development rate under future climate. The impact of rising temperature on average GY of winter wheat in sunn pest densities was negative (about 47%) while the impact of elevated [CO2] was positive (about 36%) on it. Interaction of elevated [CO2] and temperature also had a negative effect (about 18%) on average GY of winter wheat in sunn pest densities. The impact of rising temperature was positive on sunn pest while the impact of elevated [CO2] had not any effects on it. Therefore, it seems that sunn pest damage on winter wheat growth and yield will be increased (about 7%) in the future climate compared to current climate.
Jury composé de:
Harry OZIER-LAFONTAINE (Directeur de Recherche INRA - Rapporteur)
Serge SAVARY (Directeur de Recherche IRD - Rapporteur)
Thierry DORE (Professeur INA-PG - Examinateur)
Eric MALEZIEUX (Directeur adjoint UMR System CIRAD - Examinateur)
Walter ROSSING (Professor Wageningen University - Examinateur)
Jacques WERY (Professeur AGRO-M - Directeur de thèse)
A total of 106 individual farmers fields were surveyed in Zhanyi and Xundian counties (the main japonica-rice-producing areas of the Yunnan plateau) of Yunnan province. The data pertaining to pest injuries and yields was collected and analyzed using two analytical approaches. The first approach was intended to characterize relationships between injury profiles and yield levels using cluster and correspondence analyses, while the second approach was aimed at generating yield loss estimates using combinations of principal components and step-wise multiple regressions. Seven pest injury profiles (abbreviated as IN) were determined using cluster analysis; IN1, IN2, and IN3 were lower injury levels of pest combinations in seven profiles, while IN5, IN6, and IN7 were higher injury levels. Clusters of injury profiles (IN1—IN7) and yield levels (Y1—Y5) are plotted on the two first axes of correspondence analysis between patterns of injury profiles (IN) and yield levels (Y). The correspondence analysis yielded a path of increasing yield levels (Y1 to Y5) associated with varying combinations of injuries in the plane defined by axes 1 and 2. This would suggest that injury profiles (IN5, IN6 and IN7) located on the left in the factorial plane may cause more considerable damage than others (IN1, IN2 and IN3) located on the right. The principal component analysis with multiple regressions generated estimates of yield reductions due to rice diseases, insects and weeds. The results showed that injuries caused by weeds above rice canopy, stem borers (white heads), leaf folder, bacterial leaf blight, army worms, leaf blast and plant hoppers were the most damaging factors in this region. Results of this study will provide some foundations production level at the regional scale.
The rice leaffolder (RLF), Cnaphalocrocis medinalis Guenée (Lepidoptera: Pyralidae), and the white-backed rice planthopper (WBPH), Sogatella furcifera Horváth (Hemiptera: Delphacidae), are major insect pests in China and several other Asian countries. These two pests commonly occur simultaneously or in a temporal sequence. Thus, the investigation of the effect of complex infestations or temporal sequence infestations by these pests on rice yield has a practical significance for the control of these pests. The present study comprised experiments with the following four different variables in potted rice at the tillering stage: single pest species infestation, complex infestation, complete combination infestation and temporal sequence infestation (C. medinalis infestation prior to S. furcifera and S. furcifera infestation prior to C. medinalis). The results showed that the four infestations resulted in a significant decrease in 1000-grain weight (1000GW) and rate of yield loss (RYL) but an increase in blighted grain rate (BGR), with a significant positive correlation with the infestation density. However, the influences of the complex infestation, complete combination infestation or sequence infestation on the 1000GW, BGR and RYL were greater than those of the single pest species infestations but did not have addition effects, i.e., the effects of the complex infestation and combination infestation or sequence infestation on the 1000GW, BGR and RYL were less than the additive effects of the two single pest species infestations at the same densities. In the condition of the same total infestation pressure, no significant differences in the 1000GW, BGR and RYL were found between C. medinalis infestation prior to S. furcifera and S. furcifera infestation prior to C. medinalis as well as between the sequence infestation and the complex infestation.
A series of rice pest injuries (due to pathogens, insects, and weeds) were surveyed in 286 farmers' fields for major rice varieties of three rice cropping zones of Yunnan Province, Southwest China. The composition and dynamics of main pest species were analyzed, and the trend of rice pest succession in Yunnan was discussed based upon landmark publications. The results showed that the three rice cropping zones had different pest characteristics as regard to main species, dynamics and combined injuries. Sheath rot, bacterial leaf blight, rice stripe, leaf hoppers, armyworms and stem borers were serious in the japonica rice zone. Sheath blight and rice stripe were serious in the japonica-indica interlacing zone. Leaf blast, sheath blight, leaf folders and weeds above rice crop canopy were serious in the indica rice zone. False smut, plant hoppers and weeds below rice crop canopy were ubiquitous and serious in the three kinds of rice cropping zones. Many kinds of weed infestation emerged in the whole rice cropping seasons. Echinochloa crusgalli, Sagittaria pygmaea, Potamogeton distinctus and Spirodela polyrhiza were the main species of weeds in the rice cropping zones of Yunnan. Overall, levels of combined injuries due to pests in the japonica rice zone and the indica rice zone were higher than that in the japonica-indica interlacing zone. In terms of the trend of rice pest succession in Yunnan, injuries due to false smut, sheath blight and plant hoppers seemed to be in a worse tendency in all rice cropping zones of Yunnan, while dominants species of weeds in the paddy fields are shifting from the annual weeds to the perennial malignant weeds.
The study of multiple pathosystems has played a central role in the development of botanical epidemiology, leading to a number
of approaches and concepts.Multiple pathosystems are facts, which are experienced by many non-cultivated, or cultivated, plant
communities. The shapes and composition of multiple pathosystems vary in space and time because of their inherent structure
of relationships, and also in response to management. Examples of variation in multiple pathosystems are given, of groundnut
in Côte d’Ivoire, of wheat in Brittany, and of upland rice in northern Laos. Variation in the yield-reducing effects of multiple
pathosystems is discussed, including interactions among disease elements, relationships with attainable performances, and
linkages with production situations. Progress has been achieved in understanding the links between injury profiles, production
situations, and attainable performances. Questions about the functioning and consequences of multiple pathosystems are central
to defining the scientific bases for, the design of, and the implementing of IPM. The complexity of multiple pathosystems,
however, remains a deterrent, not a challenge, to many plant pathologists. Progress achieved in designing production systems
for hardy wheat in France, however, is very promising, because of the multidisciplinary science it involves, and because of
the promise to deliver it carries. The concepts of epidemiological guilds and of guilds of harmful agents are offered as perspectives
to address and manage syndromes of production and syndromes of disease.
The use of winter wheat (Triticum aestivum L.) cultivars resistant to diseases may make it possible to reduce yield losses without the need to use fungicides, which are expensive and may damage the environment. The cultivar resistances favored depend on the region considered and the nature of the most widespread diseases in that region. We have constructed a statistical linear model for the estimation of relative yield loss due to diseases, making it possible to assess the effects of winter wheat cultivars in various disease conditions. We considered the interactions “potential disease intensity × winter wheat cultivar” for four main fungal diseases: septoria tritici blotch (Mycosphaerella graminicola), brown rust (Puccinia triticina), yellow rust (Puccinia striiformis) and powdery mildew (Erysiphe graminis). This model can therefore be readily adapted to different regions with diverse combinations of these diseases. The potential intensity of each disease in each trial was calculated based on the symptoms observed on susceptible cultivars not treated with fungicide. The cultivar effect was characterized by the ratings of cultivar susceptibility to each disease and by cultivar earliness. The parameters of the model were estimated from 276 wheat cultivar trials carried out over 12 years (1991–2002) in the major wheat-growing areas of France. This model can help to choose from the cultivars resistant to several diseases (but very rarely resistant to all the diseases), those cultivars best adapted to a given environment, according to the hierarchy of potential disease intensities encountered.
A body of descriptors of the rice crop system was collected in a sample of farmers' fields in Central Luzon, Philippines, and these data were used to characterize cropping practices and pest constraints. The analysis was conducted in four steps: (1) the time-dependent, quantitative information pertaining to pests (diseases, insects, and weeds) was integrated over crop development to account for injuries to the crop; (2) classes reflecting the various distribution frequencies were developed, and the quantitative information was categorized accordingly; (3) seven patterns of cropping practices (PR) and seven types of pest profiles (PE) were characterized from two independent cluster analyses using a chi-square distance; (4) two contingency tables, yield by cropping practices (Y × PR) and yield by pest profile (Y × PE), were built and jointly submitted to correspondence analysis. Two first axes accounted for 52·2% and 25·5% of total inertia, and were used to interpret the relationships among PR, PE, and yield. A path of increasing yield levels was associated with increasingly favourable production situations, and was opposed to the accumulation of pest injuries. Cropping season, date of establishment, fertilizer input, and weed control practices were identified among the components that drive production levels. Correspondences of specific key pests, or key pest combinations, with particular production levels and patterns of cropping practices were identified. The analysis suggested that weeds and stemborers may contribute much to yield reduction. Sheath blight was closely associated with the highest yields, suggesting that (1) the disease did not contribute to any yield reduction, and/or (2) it is particularly enhanced in potentially high-yielding crops. In the latter case, control of sheath blight might result in even higher yields.
Pest management decision support systems have evolved from rudimentary single decision rules to multiple criteria optimization software. In its simplest form, a decision support tool could be a pest management threshold calculated using empirical relations and field data on a calculator. A sophisticated form would be interactive computer systems that utilize simulation models, databases, and decision algorithms, in an integrated manner, to address normative problems. Central to the decision making process in pest (insect, disease, weed) management is information on the effect that a particular pest population has on the economic output of the crop. This effect depends on crop development stage, the prevailing environment, and the crop genotype’s yield potential and ability to compensate for pest injury. In this paper, we present a conceptual framework for linking pest effects to crop models, and detail the coupling techniques used in linking pest and crop models and demonstrate, with examples, how this provides output for decision support. The crop models belonging to the CERES and CROPGRO families are used to exemplify situations for linking pest effects to crop growth and development via twenty-one links (CROPGRO) and twenty for CERESRICE. Methods are described for representing pest dynamics, since these affect the pest-crop interaction, and the kind of pest data required for input into pest-crop combination models. Five basic methods of quantifying pest dynamics are proposed — (a) Field assessment, (b) A priori assumptions, (c) Analytic modeling, (d) Pest simulation models, and (e) Use of pest simulation models interlinked with crop models. The concept and techniques for using common coupling points in multiple-pest situations are described.
A series of experiments was conducted where a range of injuries due to rice pests (pathogens, insects, and weeds) was manipulated simultaneously with a range of production factors (fertilizer input, water supply, crop establishment method, variety) in different seasons and years. These factors were chosen to represent lowland rice production situations characterized in surveys conducted in tropical Asia and their corresponding range of attainable yield. Experiments complemented one another in exploring the response surface of rich yields to yield-limiting and yield-reducing factors. The resulting experimental data base consisted of 445 individual plots and involved 11 manipulated injuries in a multiple regression model involving factors generated by principal component analysis on injuries that adequately described the variation in actual yield. One major finding was that some (attainable yield x injury factors) interactions significantly contributed to the description of variation in actual yield, indicating that some injuries (or their combinations) had a stronger or weaker yield-reducing effect, depending on the level of attainable yield. For instance, yield losses due to sheath blight, weed infestation, and rice tungro disease tend to increase, remain stable, and decrease, respectively, with increasing attainable yields. Back-computations using the principal component regression model estimated yield losses caused by individual injuries, using the mean injury levels in a population of farmers' fields surveyed across tropical Asia. The results indicate that sheath blight, brown spot, and leaf blast are diseases that cause important losses (between 1 10%) regionally. Among the insect injuries, only white heads caused by stem borers appear of relevance (2.3%) yield losses)... (D'après résumé d'auteur)
Echinochloa glabrescens Munro ex Hook. f., a common weed in flooded fields, was highly competitive with rice (Oryza sativa L.) when its seedlings were transplanted with rice seedlings. Age of the transplanted E. glabrescens seedlings had little effect on its weight at harvest and rice grain yield at different infestation levels. Average rice yield reductions from transplanted E. glabrescens ranged from 6% at the 5% infestation level to 73% at the 40% infestation level. Transplanting shock delayed vegetative growth of rice and E. glabrescens at early stages and influenced growth patterns until maturity. When all rice hills were infested, competition resulted in 11.9 and 12.8% reduction in height; 60.5 and 62.5% reduction in tiller number; 59.1 and 51.5% reduction in maximum leaf area index (LAI); 77 and 81% reduction in total dry weight; and 90 and 94% yield reduction of rice under transplanted and dibbled conditions, respectively. Panicle length and 100‐grain weight were not affected by competition. E. glabrescens was less affected by competition than rice under both planting methods
Whiteheads (WH) on modern rice varieties were studied at the Bangladesh Rice Research Institute's experimental farm during 1991–1993. About half of the stem borer infested rice tillers bore WH while the rest bore normal panicles. A deep circular cut or deep feeding in a circular band inside the stem, near the base of terminal internodes, particularly the topmost internode, was associated with WH formation. Three types of WH viz. type-I (fully emerged), type-II (partially emerged) and type-III (remaining enclosed in the leaf sheath) were recognized of which type-I was most common. Compensation for stem borer infestation at the reproductive phase was due to some unproductive tillers being converted to productive tillers, by producing more and heavier grains, and also by producing tillers from aerial nodes. Rice hills compensated for about 23% of the losses due to WH, the amount of compensation being influenced by the physical environment. Yield increases occurred at low levels of WH. Formation of WH did not influence yield and yield components of adjacent healthy hills. Over five seasons, yield losses were less than indicated by WH levels, and were nearly 5% in two out of the five seasons and close to 1 % in the other three seasons. Individual loss estimates were time and space specific so cannot be used for extrapolation to estimate regional or national losses. An analytical method for assessing yield loss due to WH was easy to use, and gave reliable estimates. A yield loss model was developed to estimate yield losses due to WH.
A methodology has been developed which allows the risk posed by pests to be addressed in the context of shifting intensification levels of rice production in Asia. The term ‘pest’ is used here to encompass any harmful agent, including pathogens, insects, and weeds. This methodology combines two separate approaches: assessing risk probability, and quantifying risk magnitude. Risk probability may be defined as the probability of a particular pest combination occurring in a given production situation. It can be derived from surveys in selected sites. Yield loss — the gap between attainable and actual yields — is taken as one measure of risk magnitude. It is assessed from a crop loss data base developed from a series of field experiments, where inputs to the rice crop and pests are manipulated independently. The analysis indicates that rice tungro virus disease, rice sheath blight, and weed infestation correspond to very high risk magnitudes. Yield losses due to pest injuries are often strongly influenced by changes in production situation, and, therefore, by variation in attainable yield. The pattern of this relationship strongly depends on the nature of the pest, however. Different pest scenarios from contrasting production situations in Uttar Pradesh, Central Luzon, eastern Thailand and the Mekong Delta can be used to illustrate the present situation, and forward hypotheses on the future need of research at the system level.
The host range of Rhizoctonia solani is so wide, and the damage caused to specific crops is so variable, that a case study may provide an appropriate approach to the general issue of measuring crop losses at the field level. Rice sheath blight provides a good example because the symptoms of the disease are fairly representative of symptoms caused by R. solani to a wide range of both monocots and dicots.
It has been hitherto assured by many researchers that the efficiency in utilizing sunlight in photosynthesis of plant community plays an important role in its dry matter production. The sunlight transmitting into plant community is generally reduced from its initial intensity, but there is considerable variation in its reduction rate among communities owing to their structural difference, which might cause consequently great influence on the efficiency in its utilization. In this respect, the form of plant in each stand which make up the structure of community could be of most importance as the fundamental factor affecting on the efficiency in utilizing sunlight. From this point of view, the form of plant in rice varieties has been studied since 1959. In the present report, as the first step, the significance of extinction coefficient in rice plant communities was examined in relation to the efficiency in utilizing sunlight and also to the form of plant. In rice plant community, it is well known that the form of plant shows considerable changes accompanied by variety, growth stage, cultivating condition, etc.. The experiment, therefore, was conducted at the period of heading and of about three weeks later, and the changes in growth attributes as well as light factors were measured with the communities of fourteen rice varieties. The results obtained would be described as follows: Light transmission rate (k) showed, in general, an exponential decrease with increase in leaf area index (F), but the considerable variation was found even among the communities of same F. This is considered to prove positively the influence of the form of plant on k. A very close negative correlation obtained between extinction coefficient (K) and F was clear enough to explain that the community with bigger F could be secured only in small K. More, indicating the remarkable influence of K on F when much amout of leaves are produced, the gradient of regression line which was dependent on the earliness of varieties turned steeper for late varieties. Suppose the dead leaves in lower part of community is caused by low illumination near or below the compensation point, the community of a smaller K is expected to have a smaller percent of dead leaves (D) because of its advantage in light transmissibility. This was clearly proved by a very close positive correlation between D and K. Evidence that K might account for the changes in the efficiency in uitlizing sunlight was given when daily increase in dry matter production of each community (ΔW) was plotted against F discriminated on K. Although the data obtained were not accurate enough for further discussion, there showed a tendency to increase both maximum ΔW (ΔWmax) and F (Fopt) correspondent to ΔWmax with decrease in K. This also proved that Fopt is decided dependently not only by intensity of incident sunlight but also by K. From these results mentioned above, K could be grasped as an important index deciding at one time the conditions in both side of light transmission and of light receiving in community, and through which, in due course, affecting its dry matter production. This also denotes that the form of plant must be studied in relation to K. Among the characteristics of rice plant so far investigated, the mean inclination of leaf adjusted by its thickness resulted in a fairly close negative correlation with K. This shows the advantage of community consisted with more erect and thinner leaves. A significant positive correlation of culm length to K might not be originated from its direct influence on K but be derived from its very close negative correlation (-0.739**) to inclination of leaf.
All types of weed community became dominant by Monochoria vaginalis (Burm.) Presl. as the nitrogen level increased. The importance value (IV.) of M vaginalis rapidly increased with increasing nitrogen level whereas the LV. of other weed species decreased. At the 10 10cm plant spacing, M. vaginalis was almost totally suppressed at all nitrogen levels. At the 20 20cm plant spacing, the degree of suppression declined with increasing nitrogen level. At the 30 30cm plant spacing, there was no suppression of M vaginalis at the highest nitrogen level. The yield obtained at the 10 10cm plant spacing when M. vaginalis was present was not significantly different from that obtained from the weed free plot for all rice cultivars while it was significantly reduced by M vaginalis competition at the 20 20cm and 30 30cm plant spacings. The difference in yield caused by M vaginalis competition was primarily due to a reduction in the number of panicles at all nitrogen levels. There was a high negative correlation between grain yield and weed weight at heading. The yield reduction due to weed competition varied depending upon the nitrogen level.
Transplanted and direct-seeded flooded rice were compared in a field experiment using identical planting density and geometry. Leaf area index (LAI), plant dry weight, and tiller number were determined at 7-14 d intervals from seeding to maturity. Canopy CO2 and H2O exchange were measured using a mobile depletion-chamber system which requires 1-1.5 minutes per measurement. The canopy CO2 assimilation rates confirmed the plant dry matter accumulation observed. Diurnal measurements of net CO2 assimilation and night respiration indicated a mild midday/afternoon depression that depended on the atmospheric vapour pressure deficit (VPD). Light response of canopy CO2 assimilation exhibited light saturation at full daylight when LAI was lower than 1. No light saturation was observed at higher LAI. Transplanting shock in transplanted rice reduced net assimilation rates and delayed foliage expansion and tillering by 15 days. Crop development was retarded by 7 days. Uninhibited growth of direct-seeded rice during the vegetative stage led to superior biological yield and tiller number at maturity while grain yield was equal to that of transplanted rice. Potential yield increase in direct-seeded flooded rice is discussed on the basis of growth kinetics and assimilate source/sink relationships.
Predawn and midday leaf water potentials and leaf osmotic potentials, soil water potentials, canopy temperatures, leaf rolling, light interception, dry matter production and water use were followed in seven rice (Oryza sativa L.) cultivars before, during and after the imposition for 10 days of different irrigation treatments by a line source sprinkler system imposed prior to flowering. The seven rice cultivars, Azucena, Tachiminori, IAC 25, IRAT 13, BPI 76, IR 20 and IR 36, represented a wide range of germplasm from traditional dryland to improved wetland types. As soil water potentials in the upper 30 cm of the soil decreased, canopy-to-air temperature differences increased, leaf rolling increased and dry matter production decreased. The wetland cultivars IR 20 and IR 36 had a greater diurnal change in leaf water potential between predawn and midday than the dryland cultivars. Canopy-to-air temperature differences and leaf rolling scores also varied among cultivars. Leaves of the dryland cultivars, particularly Azucena and IAC 25, rolled and had higher canopy-to-air temperature differences at higher waterpotetials and higher turgor pressures than the wetland cultivars IR 20 and IR 36. No differences among cultivars were observed in either the rates of dry during the period of differential irrigation. The stress treatments imposed in the pre-flowering period delayed panicle emergence by 10 days in IAC 25 which was at the boot stage of development, when stress was most severe, and by 4 to 5 days in all other cultivars, some of which emerged up to 30 days after full irrigation had been restored.The results suggest that the physiological differences observed among cultivars, particularly between wetland and dryland types, had little effect on growth and water and water use during a short period of water deficit.
This paper describes a simulation model of rice development. It consists of four submodels dealing with: (1) phenology; (2) leaf age; (3) total leaf number; (4) organ development. After combining the first two submodels, the submodel of (3) was obtained, which could be used to predict the total number of leaves on the main culm. With the synchronous relationship between development of leaves and organs, the morphological appearance and organ development for a given rice variety can be predicted. Model parameters were determined using rice data from 15 locations in the Yangtze River Valley, China for 1985–1986. The model was validated with a separate data set collected from Jiangsu province during 1988–1989. The average error in heading date for that test was 3.5 days with a correlation coefficient of 0.88.
Direct-seeded flooded rice (Oryza sativa L.) culture is becoming an increasingly popular alternative to transplanting in some regions of Asia. Two field studies were therefore conducted to compare the growth patterns of wet-row-seeded and transplanted IR64 rice as affected by rate of N fertilizer application (0 and 90 kg N ha −1 in 1987 and 0, 30, 60, 90, 120, and 150 kg N ha −1 in 1988). Row-seeded rice exhibited faster crop establishment and a more productive vegetative growth phase, because transplanting shock was absent and tiller number greater. Leaf area index was greater in row-seeded than in transplanted rice. During the reproductive and ripening stages, lower foliar N concentration depressed canopy CO 2 assimilation and crop growth rate of row-seeded rice. Lower foliar N concentration was caused by dilution and not by reduced uptake. Greatest grain yield was 9.0 Mg ha −1 with 120 kg N ha −1 applied in transplanted rice and 150 kg N ha −1 in row-seeded rice. Yield response to applied N was significant up to 60 and 90 kg N ha −1 in transplanted and row-seeded rice, respectively. The harvest index was lower in row-seeded than in transplanted rice at all N rates. Row-seeded rice had a higher N demand and an inferior ability to convert high biomass production into grain yield as compared with transplanted rice. Alternative genotypes with reduced tillering ability and limited leaf area production during the reproductive stages may improve the performance of tropical wet direct-seeded rice
Water deficits result in decreased gas exchange between plant leaves and the atmosphere. Decrease in transpiration from water stressed plant leaves is well known; however, the relationship of transpiration to uptake of N, P, and K during water stress is less well documented. Rice plants ( Oryza sativa L.) were grown in containers and subjected to water stress. Soil and plant water potential, transpiration rate, leaf area, dry wieght, uptake of N, P and K and atmospheric evaporative demand were monitored over an 18 day drying period. Decreased transpiration rate was the most sensitive variable to water stress. Transpiration of the stress treatment differed significantly from control plants when soil water potential was in the range −0.02 to −0.15 MPa. Dawn leaf water potential, alternately used to estimate soil water potential, was between −0.06 and −0.25 MPa when transpiration and N, P and K uptake of stressed plants deviated from control plants. Although extrapolation of container experiments must be done with care, our results show transpiration and nutrient uptake to be highly correlated during the development of even mild soil and plant water stress. However, interpretation of results, even in this simple system, is complex. The linear and curvilinear relationships among elements of the soil‐plant‐atmosphere continuum are discussed as aides in interpretation of results.
Increased irrigated areas, availability of short-duration modern rices (Oryza sativa L.) and herbicides, and increasing labor cost have motivated Asian farmers to shift from transplanting to direct seeding of lowland rice. Information on cultural requirements of direct seeded lowland rice in Asia is limited. Thus, two field experiments were conducted during the dry season on a Vertic Tropaquept soil to evaluate various cultural practices of direct seeded rice, and to compare the growth characteristics of direct seeded and transplanted rice. Direct seeding increased vegetative growth and shortened crop duration by more than 1 wk. Broadcast seeded rice gave the highest tiller number, followed by row seeded and hill-wise dibbled rice. Relative tillering rate was significantly correlated with relative growth rate. Greater vegetative growth of direct seeded rice decreased tissue N concentration which in turn impeded sink formation. Grain yield of direct seeded rice was significantly correlated with spikelet number per panicle but not with panicle density, while the reverse trend was observed in transplanted rice. Band placement of fertilizer N gave significantly higher canopy CO2 assimilation rates and grain yield than did conventional fertilizer application methods in both direct seeded and transplanted rice. By increasing N availability to the plant, band placement allowed the use of a onethird to two-thirds N split application which increased plant N concentration during the reproductive stage, particularly in direct seeded rice. Leaf area index (LAI) was higher in direct seeded than in transplanted rice. Canopy photosynthesis was limited by LA1 during the vegetative growth stage and by foliage N concentration during the reproductive stage.
Contribution from the Agron. Dep. (IRRI). Supported by the German Agency for Technical Cooperation (GTZ), West Germany.
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A protocol for characterizing patterns of rice cropping practices and injuries due to pathogens, insects, and weeds was developed and used in six sites in tropical Asia covering a wide range of environments where lowland rice is cultivated. The data collected in a total population of 456 individual farmers' fields were combined to site-specific weather data and analyzed using non-parametric multivariate techniques : cluster analyses with chi-square distance and correspondence analyses. The main results are : (1) patterns of cropping practices that are common across sites can be identified ; (ii) conversely, injury profiles that are common across sites can be determined ; (iii) patterns of cropping practices and injury profiles are strongly associated at the regional scale ; (iv) weather patterns are strongly associated with patterns of cropping practices and injury profiles ; (v) patterns of cropping practices and injury profiles allow for a good description of the variation in actual yield ; and (vi) patterns of cropping practices and injury profiles allow for a good description of the variation in actual yeild; and (vi) patterns of cropping practices and injury profiles provide a framework that accurately reflects weather variation and site diversity, and reliably accounts for variation in yield. The mean estimated yield across sites (4.12 t/ha) corresponds to commonly cited averages in the region and indicates the potential for increased productivity with better management practices, especially an improved water supply. Injuries due to pests are secondary compared with other yield-limiting factors. Injury profiles were dominated by stem rot and sheath blight (IN1) ; bacterial leaf blight, plant hoppers, and leaf folder (IN2) ; and sheath rot, brown spot, leaf blast, and neck blast (IN3)... (D'après résumé d'auteur)
The performance of non-flooded rice was compared with that of the 2 major cereal crops grown in south-eastern Queensland. Three cultivars of rice and 1 cultivar each of sorghum and maize were grown either under weekly sprinkler irrigation or under non-irrigated conditions after establishment. Grain yield, total dry matter and light interception were measured to determine the responses of the cereal species to soil water deficits. Under weekly irrigation, the low grain yield (ovendry) of rice (680 g m-2 in the highest yielding cultivar compared with 1240 g m-2 for sorghum and 1060 g m-2 for maize) was mostly due to inefficient conversion of solar radiation to dry matter. Both total radiation interception and harvest index were similar among the 3 species. Under dry conditions, rice intercepted less radiation than did the 2 cereals. There was also a larger reduction in the efficiency of conversion of solar radiation to dry matter in rice, and hence total dry matter production was severely reduced. Grain yields under dry conditions were 52, 58 and < 10% of the corresponding yields in the wet trial in maize, sorghum and the 3 rice cultivars, respectively. In rice, dry conditions delayed heading, and consequently grain set was severely affected in the 2 quick maturing cultivars, while the long season cultivar (IR43) failed to head during the experimental period of 163 days. At maturity, the rice cultivar, Shinhakaburi, bred for upland conditions, produced a significantly higher biomass than the lowland cultivar, Labelle, or the upland cultivar IR43.
Rice crops were grown under sprinkler irrigation, on a free-draining soil, to examine the feasibility of growing rice without flooding in summer-rainfall regions of coastal Queensland. Three cultivars with different heading times were each sown on 3 different dates to determine the optimum time of planting in south-eastern Queensland. Dry grain yield was about 600 g m-2 for all 3 cultivars when sown in early summer. These early sown crops received a total of about 375 mm of irrigation water, all of which was applied during early stages of growth. Grain yield was much less in the later sowings, although total biomass production was relatively high. Low temperatures in autumn appear to have affected grain set of the late sown crops.
A field experiment was conducted to analyze the effect of crop duration on growth and the yield-formation process in tropical lowland rice. Three semidwarf rices (IR58, IR64, and IR29723-143-3-2-1) of different growth durations were either transplanted or broadcast-seeded, and grown under different nitrogen fertilizer regimes. Dry-matter accumulation and N concentration of various plant organs, leaf-area index (L), tissue death, tiller number, plant height, and canopy photosynthesis were recorded periodically. Crop duration increased plant biomass but did not significantly affect grain-yield. Plant N uptake was not affected by crop duration. Direct seeding gave grain yields superior to those of transplanted rice using short-duration IR58, but equal or lower grain-yield using the medium- and long-duration varieties. Growth and development were delayed, and tillering and foliage growth were reduced in transplanted rice due to transplant shock. Tiller production and abortion were a function of relative growth rate. Tissue death occurred during the reproductive phase and depended on L and foliage N concentration. IR58 had the highest L (up to 11.6) and IR64 the lowest. Direct-seeded rice had higher L and dry-matter during vegetative and reproductive growth, but lower foliage N concentration, than had transplanted rice. Growth during ripening was negatively affected by high biomass and L at flowering but enhanced by high foliage N concentration. It is concluded that grain-yield is impeded by high biomass at flowering in direct-seeded rice, particularly in long-duration varieties. High biomass at flowering, however, may enable further yield improvement if N partitioning and foliage expansion patterns are modified.
The relationship between rice cropping practices, biotic constraints, and rice yield levels in the eastern part of Uttar Pradesh, India was studied. The investigation was conducted at the centre of a geographic area representative of the rice-wheat system of South Asia, and covers a large diversity of production situations undergoing intensification. A survey procedure was used to collect data for 3 consecutive years (1993–1995) in 251 individual farmers' fields. Two analytical approaches were used, the emphasis shifting from yield determining variables, that are mostly qualitative in nature, to quantitative, and predominantly yield-reducing, variables. The first approach was intended to characterize relationships among variables using cluster and correspondence analyses, while the second approach was aimed at generating yield loss estimates using combinations of principal components and step-wise multiple regressions. Seven patterns of cropping practices were distinguished, reflecting a wide variation in production situations, especially in terms of use of fertilizers and manure, and degree of water control. Six types of disease profiles, four insect injury profiles, and four weed infestation patterns were identified. Correspondence analysis, based on patterns of cropping practices and injury profiles, yielded a path of increasing attainable yield associated with varying levels of intensification and combinations of injuries. The use of principal component analysis with multiple regression generated estimates of yield reductions due to rice diseases, insects and weeds. The analysis highlighted the effects of changes in patterns of cropping practices on injuries caused by weeds, brown spot, sheath blight and dead hearts. The significance of these findings with respect to current and future IPM needs is discussed.
The efficiency of crop production is defined in thermodynamic terms as
the ratio of energy output (carbohydrate) to energy input (solar
radiation). Temperature and water supply are the main climatic
constraints on efficiency. Over most of Britain, the radiation and
thermal climates are uniform and rainfall is the main discriminant of
yield between regions. Total production of dry matter by barley,
potatoes, sugar beet, and apples is strongly correlated with intercepted
radiation and these crops form carbohydrate at about 1.4 g per MJ solar
energy, equivalent to 2.4% efficiency. Crop growth in Britain may
therefore be analysed in terms of (a) the amount of light intercepted
during the growing season and (b) the efficiency with which intercepted
light is used. The amount intercepted depends on the seasonal
distribution of leaf area which, in turn, depends on temperature and
soil water supply. These variables are discussed in terms of the rate
and duration of development phases. A factorial analysis of efficiency
shows that the major arable crops in Britain intercept only about 40% of
annual solar radiation and their efficiency for supplying energy through
economic yield is only about 0.3%. Some of the factors responsible for
this figure are well understood and some are immutable. More work is
needed to identify the factors responsible for the large differences
between average commercial and record yields.
Potato yield and foliage loss expectations for solitary and concurrent infestations of potato leafhopper (Empoasca fabae), early blight (Alternaria solani), and Verticillium wilt (Verticillium dahliae) were developed with a crop growth simulation model and compared to data obtained when these same organisms were studied in a factorially arranged field experiment (...)
Four methods of measuring crop production are reviewed in the context of different survey objectives. A popular technique, crop cutting, tends to overestimate yields and does not produce good estimates of individual plots. For high accuracy, harvest of the whole plot is the best method. If statistics of regional production are required output can be sampled after harvest, or, if the farmer harvests in consistent units, his own estimate can be taken. Limited evidence shows that farmers' estimates may be no more biased than crop cutting, but require fewer resources and supervision. There is no best method. The method used must be chosen for the purpose of the study. Whichever method is chosen a distinction should be made between biological and economic yield and correction must be made for threshing and moisture content.
Integrated pest management (IPM) in rice has been mainly applied to irrigated, lowland paddy rice ecosystems which generally use more inputs and have higher yields ha−1 than rainfed ecosystems. Large scale implementation of IPM was stimulated in the 1970s in several Asian countries by pest resurgences resulting from indiscriminate insecticide use. Legislation has been enacted in at least five countries to support IPM and by 1992, approximately 0.05% of Asian farmers had received training in IPM. In at least two countries, adoption of IPM as national policy has resulted in large savings from reduced importation of pesticides with no reduction in total rice output or productivity. The inter-country programme on rice IPM of the Food and Agriculture Organization (FAO), the United Nations, the International Rice Research Institute and national programmes have all contributed to promoting rice IPM. Host plant resistance, deployed with location-specific cultural practices that conserve natural enemies, remains the basis for sustainable, low-cost rice IPM. Thresholds, monitoring and forecasting systems have little or no impact on farmer practice in the tropics; farmer group learning appears to be the most effective way towards IPM adoption.Manejo integrado de plagas en el arroz
A line-source sprinkler was assessed as a means for testing rice cultivars in the field, and various aspects of crop growth were examined in relation to drought screening techniques. The experiment consisted of seven rice cultivars and four replications where the line source sprinkler provided the plots with a water supply that diminished linearly with distance from the line. Up to the time of panicle emergence two tall upland cultivars produced more dry matter than lowland cultivars, particularly at lower rates of water application. Soil water measurements for one upland and two lowland cultivars, however, showed that this was due to increased water extraction from the soil profile, and that dry matter production until panicle emergence was a function of water used. There were no differences between these three cultivars in water use efficiency.Heavy rains at and after panicle emergence replenished the soil water. The lowland rice IRI529-430-3 was the most efficient in grain production, with the highest harvest index. The two tall upland cultivars Kinandang Patong and IAC 47 were shown to have deeper root systems, but their relative grain yield at low compared to high water supply was poorer than for IRI529-430-3. Other experiments have shown that this lowland rice has a stable yield across a range of upland environments, and the implications of this are discussed with reference to techniques of selecting for drought resistance. It is emphasised that vegetative assessments used in drought screening must be interpreted in relation to grain yield.
Rice crops grown under irrigated conditions were inoculated withPyricularia oryzae during early growth stages to study the effect of leaf blast on yield formation. The inoculations led to severe epidemics of leaf blast around maximum tillering, characterized by the presence of typical blast lesions and an accelerated senescence of heavily infested leaf tissue. Leaf blast led to a prolonged tillering and a delay in flowering and maturity.
Crop growth rate and leaf area formation declined sharply during establishment of the disease and continued to be reduced till maturity. This resulted in a marked reduction of total dry matter production and grain yield. Dry matter distribution was not affected. Leaf blast reduced spikelet number, 1000 grain weight, and the fraction filled grains. From this last observation it was concluded that the reduction in grain yield was exclusively source determined.
Nitrogen uptake of the inoculated crops before flowering was reduced compared to the N uptake of the control crop, but shoot N content of the inoculated crops at flowering was higher. Uptake of nitrogen after flowering was negligible in both healthy and inoculated crops. Redistribution from vegetative tissue was therefore the main source of N for grain growth. The higher N content of the shoot organs in the inoculated crops during ripening led to the conclusion that the reduced N uptake was not responsible for the yield reduction observed. Consequently, the reduction in grain yield was solely determined by a reduced carbohydrate supply.
Of 16 barley cultivars the partial resistance to leaf rust, Puccinia hordei, was measured in field plots isolated from each other by at least 30m of winter wheat. The partial resistance caused by a lower infection frequency, a longer latent period, a lower sporulation rate or a shorter sporulation period, or a combination of these, varied greatly among the cultivars, of which 9 were tested in 1973 and all 16 in 1974. The correlation with the assessments obtained from small, adjacent plots, where the epidemic is determined by the spreader rows and the susceptible cultivars, was very high (r=0.95 to 0.96), The correlation with latent period in the seedling stage was moderate (r=0.57), with latent period in the young flag leaves very high (r=0.92). The assessments obtained from the small, adjacent plots, although evaluating the partial resistance of the cultivars in the right order did under estimate the partial resistance very strongly. As cultivar testing for variety-lists is generally done in this way, though the plots may be larger, the values for partial resistance entered into such variety-lists may often under estimate the resistance seriously.
A series of six experiments was conducted to study the relationships between production situation, injuries, and damage in the groundnut-rust-late leaf spot pathosystem. The production situation, represented by attainable yields, was varied by replicating the experiments over seasons and incorporating several input factors at different levels. Injuries, represented by log-transformed areas under disease progress curves, were manipulated by means of inoculations and fungicide applications. The resulting database was used to develop damage functions, represented by yield and relative damage response surfaces, using multiple regression analysis. The corresponding equations indicate significant interactions between attainable yield and injuries on actual yield and relative damage. Further analysis indicates that injury-damage relations differ in rust and late leaf spot: whereas damage due to late leaf spot was mainly related to reduction of green leaf area and defoliation, damage due to rust was attributable to different mechanisms in addition to reduction of green leaf area. The negative interaction between the injurious effects of the two pathogens was ascribed to this difference.
A demographic model for rice (Oryza sativa L.) growth and development as affected by temperature and solar radiation under non-limiting water and nutrient conditions is presented. A distributed delay model is used to describe the dynamics of tiller production and culm, leaf, root, and grain mass growth. Photosynthesis is simulated with a modified functional response model from predation theory, an approach successfully applied to estimate energy (dry mass) acquisition in several crops and across different trophic levels. The ratios of carbohydrate supply and demand scale all growth and production rates of plant subunits. The model results are compared to 1984/85 and 1986/87 data from Madagascar. The effects of transplanting delays and varying planting densities on rice growth and yield are explored.
The physiological and morphological responses of two semi-dwarf lowland rice cultivars to transient drought were studied in three greenhouse experiments. These responses were related to root-zone soil water status for use in a rainfed-rice simulation model. Results were very similar for both varieties. Drought responses in young plants occurred at a lower soil water status than in older plants. The first observed effect in a drought period in the vegetative phase was a decline in leaf expansion rate compared to well-watered plants. Leaf expansion stopped completely with root-zone soil water pressure potential h in the range −50 to −250 kPa, depending on crop age and growing season. The rate of transpiration, corrected for differences in LAI, remained roughly equal to that of well-watered plants in the range 0 > h > −100 kPa, depending on crop age. As the soil water status declined further, relative transpiration rate decreased with increasing values of log(|h|), following a logistic function. Leaf rolling and early senesence started at h < −200 kPa or lower and were linearly related with log(|h|). Yield differences between plants that were transiently stressed in the early vegetative phase and well-watered plants were not significant. However, flowering and maturity were delayed. Severe drought in the reproductive phase resulted in large yield reductions, mainly caused by an increase in the percentage of unfilled grains and also in grain weight.
Pest problems are complex and a systems view contributes to understanding their causes and assessing possible solutions. Systems approaches provide a framework for systematic analysis, synthesis and design of agricultural systems at different levels of aggregation. In systems research, the real world is divided into systems, the essence of which is captured in models. The choice of system boundaries is determined by the objectives of the study. Explanatory simulation models are based on the distinction between the process level, or explanatory level, and the system level, or level to be explained. In the course of systems research, three phases are distinguished, each with a different product: problem identification results in a conceptual model, increasing production ecological insight results in a comprehensive model, and systems design results in options and their relation to objectives of management. Various qualitative and quantitative tools are available in each of the phases. Opportunities and constraints for using systems approaches in all three phases are discussed, using illustrations at the crop, farm and regional levels.
A simple whole-plant level potato growth model was developed that accumulates and partitions dry matter into four state variables—leaves, stems, roots and tubers. Daily growth is computed from a function of the total solar radiation, the proportion of total radiation intercepted by the crop, temperature and soil water status. Dry matter is partitioned using modified Michaelis-Menten equations. At initialization, the seed piece size, plant and row spacing, and the values for the dry matter partitioning parameters are required. After initialization, the inputs required are the daily minimum and maximum temperatures and the daily soil water potential. Daily site-specific, seasonal radiation is presently estimated from a sine function. Model parameterization and output plotted against field data are presented for two cultivars, each planted a total of three times in two seasons. The model is intended for use in analyzing the dynamics of yield under variable pest pressures and environmental conditions. Model sensitivity, behavior and use in relation to modeling yield loss due to pests are discussed.
Developing integrated pest management systems requires detailed quantitative knowledge of the interactions between the crop and its pests (insects, diseases and weeds) to identify intervention points and to predict effects through damage relationships or yield loss functions. Because of the complexity of such systems, models at different levels of detail are needed to provide insight into the mechanisms of crop-pest interactions. These insights may form the basis prediction and decision support systems.Several approaches have been developed to link pest models with crop models. Several coupling points for pest-crop models were identified for different pest-crop combinations. These were modeled on the basis of experimental research on the quantification of the physiological damage mechanisms. Examples are given of the use of the models that simulate the impact of pests on crops as an important research tool. Recently, approaches to simulate multiple pest damage have been developed and evaluated. Special attention is given to the use of such models to understand and predict the effects of weeds on crops.The approach of linking pest and crop models provides a tool with which to explore the dynamics of the interactions and optimize pest management strategies.
The study described in this thesis focuses on a quantitative understanding of the effect of leaf blast on growth and production of a rice crop, based on insight in the physiological processes underlying damage. For this purpose, experimental research was conducted at two levels of integration: plant and crop. Research was first conducted at the plant level and focused on the effects of the disease on photosynthesis and respiration. To determine the consequences of these physiological disturbances for crop growth and production, submodels for the effects of leaf blast on photosynthesis and respiration were constructed and introduced in a mechanistic model for crop growth. Experimental research at the crop level was used to validate and improve the extended crop growth model. At the same time the model was used to analyse the results of the field experiments. This interaction resulted in a better understanding of yield reduction in rice due to leaf blast and in a model that can be used to estimate yield reduction due to leaf blast for various epidemics under variable environmental conditions.