![Relationship between leaf area index (LAI) and canopy cover (CC) for dryland corn grown at Akron, CO, and Sidney, NE (Eq. [3]).](https://www.researchgate.net/profile/Dc-Nielsen/publication/260337366/figure/fig2/AS:297031911395336@1447829334575/Relationship-between-leaf-area-index-LAI-and-canopy-cover-CC-for-dryland-corn-grown_Q320.jpg)
![Relationship between leaf area index (LAI) and canopy cover (CC) for dryland winter wheat grown at Akron, CO, and Sidney, NE (Eq. [4]).](https://www.researchgate.net/profile/Dc-Nielsen/publication/260337366/figure/fig3/AS:297031911395337@1447829334592/Relationship-between-leaf-area-index-LAI-and-canopy-cover-CC-for-dryland-winter-wheat_Q320.jpg)
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Canopy Cover and Leaf Area Index Relationships for Wheat, Triticale, and Corn
Abstract and Figures
Previously collected data sets that would bc useful for calibrating and validating Aqua Crop contain only leaf area index (LAI) data but could be used if relationships were available relating LAI to canopy cover (CC). The objective of this experiment was to determine relationships between LAI and CC for corn (Zea mays L.), winter wheat (Triticum aestivum L.), and spring triticale (x Triticosecale spp.) grown under dryland or very limited irrigation conditions. The LAI and CC data were collected during 2010 and 2011 at Akron, CO, and Sidney, NE, using a plant canopy analyzer and point analysis of above-canopy digital photographs. Strong relationships were found between LAI and CC that followed the exponential rise to a maximum form. The relationship for corn was similar to a previously published relationship for LAI <2 m(2) m(-2) but predicted lower CC for greater LAI. Relationships for wheat and triticalc were similar to each other.
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... Each of these parameters was measured in triplicate. Canopy cover was estimated using digital photography (Nielsen et al., 2012). Measurements began seven days after germination and were recorded on a weekly basis for 5 weeks. ...
Anthropogenic activities leading to erosion-induced topsoil loss still present a pertinent threat to maize (Zea mays L.) productivity across the U.S. Corn Belt region. Using soil amendments has been proven effective in reversing the effects caused by erosion-induced topsoil loss and restoring agronomic productivity and yields to pre-eroded conditions. The current study investigated the impact of simulated erosion and repeated amendment application on agronomic productivity and yields over five growing seasons for a 23-year-old experiment at two central Ohio sites (Waterman Farm: WF; Western Station: WS). Simulated erosion/deposition was employed in 1997 to create three incremental topsoil depths (TSD) (20 cm topsoil removed (TSD-0); 0 cm topsoil removed (TSD-1); 20 cm topsoil added (TSD-2). Three soil amendments (inorganic, synthetic N fertilizer (INO); organic, compost manure (MAN); no amendment (CON)) were investigated for their ability to restore productivity in these cropping systems. Greater TSD produced higher germination counts and crop stand and yielded greater canopy cover during the initial five weeks after germination. The MAN amended soils observed higher values for the productivity parameters during the 2016-2020 growing seasons. Grain and biomass yields were 34-51% and 5-12% lower, respectively, for the TSD-0 level and 4-10% and 3-6% greater, respectively, for the TSD-2 level when compared to the undisturbed, TSD-1 level. Relative biomass yield losses (% per cm topsoil depth lost) were 0.921 for the WF site and 0.254 for the WS site, corresponding to average biomass losses (Mg ha − 1 cm − 1 yr − 1) of 0.083 and 0.026 for each site. Relative grain yield losses (% per cm topsoil depth lost) were 1.29 for the WF site and 0.514 for the WS site, equating to mean grain losses (Mg ha − 1 cm − 1 yr − 1) of 0.083 and 0.026 for each site. The rehabilitative capacity of the soil amendments followed a trend of INO > MAN at the WF site and MAN > INO > CON at the WS site. The deleterious effects of erosion on grain and biomass yields were found to be reversed with the addition of the INO and MAN amendments at the WS site due to improvements in soil health and SOC pools. The MAN amended soils produced grain and biomass yields that were nearly 2.5% and 5.6% greater than the INO amended soil when compared to the reference treatment (TSD-1-CON). This study demonstrates that sustainable soil management practices coupled with annual soil amendment addition can mitigate the deleterious effects of erosion and rebound maize yields to pre-eroded conditions for soils experiencing severe topsoil loss.
... Table 1 only contains the observations that are relevant to this work. The CC parameter was derived from LAI measurements using the conversion formula of Nielsen et al. (2012). Fixed model inputs can be found in Table A.1 in the Appendix. ...
The AquaCrop model has become well established in the crop modelling community. Its comparatively low input data requirements suggest a competitive advantage over other crop simulation tools. However, in data-scarce regions such as the Global South, AquaCrop is of limited use. Constraints are associated with difficult measurements that are necessary to derive model input parameters and calibration variables. This expresses the need for a user-friendly tool that can provide insight into model uncertainties and minimum input requirements. While AquaCrop neither enables the modelling of multiple parameter combinations nor integrated sensitivity or uncertainty analysis, these functionalities are provided by the freely available Sensitivity Analysis For Everybody (SAFE) toolbox. This thesis developed a tool to automate AquaCrop-OpenSource (AOS) model set-up and communication with selected SAFE functionalities. The Elementary Effects (EE) method was used for sensitivity analysis, to detect and exclude non-influential parameters from AOS model set-up. Second, the effect of the Generalized Likelihood Uncertainty Estimation (GLUE) method on parameter input value ranges was investigated. GLUE was also applied to estimate minimum AOS calibration requirements with respect to field measurements. Based on these results, the relationship between model reliability and stress-induced biomass loss was elucidated. The tool was tested on data from a wheat growth experiment conducted in the catchment of the Ganga river, in Kanpur, India, during one season in 2018. The analysis could identify only few model-insensitive parameters, and was unable to substantially reduce the value range for most parameters. However, the results also indicated that reliable AOS calibration only requires accurate canopy cover observations, which eliminates the need for complicated in-season soil water content measurements. Based on the revealed model output uncertainties, a likely range of stress impact on crop development could be simulated. Thus, the proposed tool demonstrated its suitability for simplifying model set-up and analyzing output uncertainty. However, given the partially unreliable input data which were used in this study, the results should be interpreted with caution and require further investigation.
... LAI describes water consumption in terms hydrologic processes, which control the amount of water intercepted by leaf area. This vegetation variable plays an important role in hydrologic processes and water budgeting in catchments [13][14][15][16][17][18]. Concerning the influence of LAI on eco-hydrology processes, investigating its changes on a catchment scale is vitally important [19] as it enables hydrologists to estimate accurate water budgets under climate change scenarios. ...
Monitoring vegetation change and hydrological variation is crucial as they are useful means of appraising the ecological environment and managing water resources in water-resource-sensitive regions. The leaf area index (LAI) describes water consumption in hydrologic processes and is an important vegetation variable for water budgeting in catchments. As part of the Soil and Water Assessment Tool (SWAT), LAI is a significant parameter, which links vegetation dynamics with the hydrological cycle. In the current study, we have aimed to describe the Lhasa River (LR) cumulative streamflow based on simulation scenarios obtained with the SWAT model. After dispensing a heterogeneous LAI time series developed by MODIS NDVI as a source of data at the HRU level (SWAT-synthetic LAI scenario), the study has produced a better representation of LR cumulative streamflow in terms of the selected evaluation criteria, encompassing the SWAT-baseline (SWAT-B scenario)-simulated and SWAT-built-in LAI-influenced (SWAT-LAI scenario) LR cumulative streamflow. The study has revealed a close relationship between the observed and the SWAT-SLAI-scenario-generated LR streamflow, with a similar MK trend for the study time span. The LAI has been found to share a close relationship with LR streamflow, as both the LAI and LR streamflow are found to be influenced by the rainfall received in the Lhasa River Basin (LRB). The study is instrumental in understanding the association between LR streamflow, vegetation change, and the climatic conditions of the Lhasa River Basin (LRB).
... Canopy cover (CC) was derived from measured leaf area index (LAI, cm 2 cm − 2 ) using an exponential function parameterized for wheat by Nielsen et al. (2012): ...
Determination of relative root-zone water depletion (RRWD) thresholds to trigger irrigation is crucial to create optimal irrigation schedules targeting maximum yield and/or water productivity with limited water supply for a crop. In this study, a numerical procedure to determine RRWD thresholds was developed through coupling AquaCrop software with genetic-simplex algorithms. Using a two-year field lysimetric experiment for winter wheat conducted in the North China Plain (NCP), AquaCrop adequately simulated canopy cover, final above-ground biomass, grain yield, seasonal evapotranspiration, and soil water storage, with the normalized root mean squared error (NRMSE) smaller than 15 % and determination coefficient (R 2) larger than 0.84. The global optimum range of RRWD thresholds was preliminarily determined using the genetic algorithm, and subsequently final RRWD thresholds were optimized by fine tuning using the simplex algorithm. The RRWD threshold combinations (composed of the RRWD thresholds to trigger different sequential irrigation events) for varying number of irrigation events (i.e.1-4) were optimized based on 39 years of historical meteorological data, and the effects of climate change on the optimal crop yield (Y a, opt), water productivity (WP opt), and the combinations of optimized RRWD threshold (RRWD opt) were investigated. The results indicated that both Y a, opt and WP opt generally increased with time showing a tendency of gradually elevated annual CO 2 concentration and seasonal average effective temperature. Irrespective of the number of irrigation events during the winter wheat growing season, the differences of RRWD opt for different combinations of irrigation sequence and event in the same kind of hydrological year were relatively small, with a coefficient of variation consistently less than 23 % and a mean of 8 %. When combinations of mean RRWD opt were applied into AquaCrop to trigger irrigation for winter wheat in various hydrological years, the simulated yield (Y a, sim) and water productivity (WP sim) under 1-4 irrigation events were found to be comparable to their respective optimums (Y a, opt and WP opt), with all the values of Y a, sim (WP sim) falling in the range of 92 %Y a, opt (90 %WP opt). Therefore, the mean RRWD opt should be helpful to formulate rational irrigation management strategies of winter wheat under changing climatic conditions in the NCP.
... LAI was converted to canopy cover (CC) in % using the empirical relationship between CC and LAI for wheat crops using (4) [17]. ...
Lake Chad region is currently experiencing trending issues. Climate change is among the major influencers of these issues that require inevitable consideration for a sustainable ecosystem. Various crop models have been developed and employed in various environmental conditions and management practices, which are cheaper and easier than field experiments. Therefore, crop models could be used to simulate various water management strategies and suggest suitable options. In this work, the FAO AquaCrop model has been evaluated to simulate deficit irrigation (DI) scenarios for wheat crops using data generated from a field experiment. The model simulated grain yield (GY), biomass yield (BMY), biomass production (BMP) and canopy cover (CC) adequately during its calibration and validation. However, its performance in simulating water productivity (WP) and actual crop evapotranspiration (ETa) was low with average r2, NRMSE, model efficiency (EF) and Willmot Index of agreement (d) of 0.58, 11.0 %, -1.40 and 0.69 respectively. The study of DI scenarios using the model revealed that the application of DI throughout the growth stages of the crop could significantly affect GY and WP. The highest GY and WP of 5.3 t/ha and 1.50 kg/m3 were respectively obtained at the application of full irrigation (T100). Increasing DI beyond 20 % depressed both GY and WP significantly. However, increasing the irrigation interval from seven to ten days did not affect GY, thereby improving WP from 1.28 kg/m3 to 1.38 kg/m3. Therefore, applying an 80 % irrigation requirement throughout the wheat growing season at 10-day intervals could save 25 % of irrigation water, a valuable strategy to improve irrigation water use without significant yield reduction. Furthermore, irrigation-related scientists and managers can use the validated model to decide the current and future irrigation water management for similar wheat varieties in similar environmental conditions.
... The estimated ETp was further separated into potential daily evaporation (Es) and transpiration (Tp) based on the variation in the leaf area index (LAI) [41]. The LAI variations in the studied winter wheat were estimated using an LAI-CC relationship formula which is applicable to a wide range of field conditions [42]. The distribution of root growth defined in HYDRUS relies on the model reported by Vrugt et al. [43], and the root parameters of maximum depth and density were adopted from field observations at harvest assuming a linear root growth system for the model setting (0-40 cm region with a maximum root density at a 10 cm soil depth). ...
Soil salinization induced by shallow saline groundwater in coastal areas can be managed using subsurface pipe drainage (SPD) for agricultural land reclamation. However, a reasonable SPD system layout should comprehensively consider local hydrological conditions and crop physiological characteristics based on long-term model evaluations. The objectives of this study were to test the applicability of a crop growth model (AquaCrop) for simulating winter wheat growth in SPD-applied fields by employing the water table behaviors predicted by the soil hydrologic model HYDRUS. Model calibration and validation based on field observations suggested that HYDRUS accurately predicted the distributions of soil water–salt dynamics, and the seasonal variations of canopy cover and biomass production predicted by AquaCrop were close to the measured values. The simulation scenarios considering the long-term effect of groundwater salinity (10.53, 21.06, and 31.59 g L−1 for low, medium, and high levels), drain spacing (10, 20, 30, 40 m, and no-SPD), and precipitation category (dry, normal, and wet year) on soil solute transport, grain yield (GY), water productivity (WP), and groundwater supply (GS) were further explored using a combination of HYDRUS and AquaCrop. The simulation results indicated that narrowing the drain spacing could improve the desalination performance of SPD, but there was no continuous downward trend of soil solute concentration during the long-term application of SPD when groundwater salinity was constant. The SPD application could improve grain yield by 0.81–1.65 t ha−1, water productivity by 0.13–0.35 kg m−3, and groundwater supply by 6.06–31.03 mm compared to the no-SPD scenarios, but such increases would be less pronounced in dry years with groundwater salinity at the low level. This study demonstrated that the co-application of hydrologic and crop growth models is a feasible method for revealing the effects of SPD on agricultural land reclamation in coastal areas.
... Unmanned aerial vehicle (UAV) is capable of gathering a diverse set of data such as canopy cover (CC), canopy height (CH), NDVI, excess green index (ExG), and normalized difference red edge index (NDRE) on many phenotypes at a shorter time to evaluate drought effects (Bhandari et al., 2021). Further, the correlation of UAS-based data such as NDVI with biomass and grain yield, percentage of green canopy with leaf area index and leaf area, and senescence patterns under drought were reported in assessing wheat (Nielsen et al., 2012;Hassan et al., 2019;Bhandari et al., 2020). The accuracy of the UAS spatiotemporal data depends on the sensor type used in the unmanned aerial vehicle. ...
... Using AquaCrop, you can model the development, biomass production, and harvest of grassy crops [7]. In AquaCrop, the growth of the plant's leaves is measured by the green canopy cover (CC) and not by the Leaf Area Index [8]. The production of above-ground biomass is related to the total quantity of crop transpiration. ...
This research was carried out to simulate wheat growth under deficit irrigation across two cropping years (2020-21 and 2021-22) at Prayagraj, Uttar Pradesh India. Crop growth simulation models are useful for evaluating the impact of water scarcity on crop productivity and crop yield. The AquaCrop model is one of these available options and this model is capable of simulating water productivity, grain yield, biomass yield, and canopy cover. Major inputs for the model in this investigation are climate, soil characteristics, plant attributes, and the management of crop cultivation. The results of the simulation revealed that the model accurately simulates grain yield, water productivity, biomass yield, and canopy cover at different amount of irrigation. The simulated and observed grain yield coefficients of determination (R2) were 0.96 and 0.95, respectively. The model simulation performed well (RMSE=0.131, NRMSE=3.75) and model efficiency (EF) was 0.99. Simulated and observed water productivity is equivalent at different amount of irrigation regimes. The model simulation performed well (RMSE=0.106 and NRMSE=5.00). Simulated and observed water productivity had EF of 0.99 and R2 of 0.96. The model simulation of biomass under different amount of irrigation varied between RMSE ranged from 0.492 to 0.857, NRMSE ranged from 9.239 to 17.513 and EF ranged from 0.97 to 0.99. Coefficients of determination (R2) of simulated biomass yield and observed biomass of all treatments were 0.99. It was determined that the simulated canopy cover for all treatments at different amount of irrigation had successfully adapted to the observed canopy cover. Thus, the RMSE varied from 13.344 to 18.974, the NRMSE from 20.770 to 33.223, and the EF from 0.91 to 0.96 for model simulations treated to various interventions. Coefficients of determination (R2) of simulated canopy cover for all treatments were 0.97, although R2 of observed canopy cover for various treatments ranged from 0.88 to 0.92.
This study was designed to determine the canopy cover, c of the tropical forest and explore its effects on the interception loss amount. The study area was a reserved forest Lagong Hill Forest Reserve, Kepong, Selangor, Malaysia, where two plots with an area of 400 m2 (20 m x 20 m) each have been set up to collect the data. In order to determine the measured interception loss, rainfall, throughfall, and stemflow data have been measured on site. The tree with a diameter at breast height (dbh) of more than 10 cm was selected in collecting the stemflow data. Twenty-five (25) locations have been chosen for the digital hemispherical photographs to be taken. The forest standing of the study area has been visualised by Stand Visualization System (SVS) method, and WinSCanopy 2009a and RGBFisheye.exe application software has been used to obtain the canopy cover values. In this study, the correlation between canopy cover and interception loss were obtained for both plots. It is found that the Lagong Hill Forest Reserve varied has a compact canopy density with canopy cover up to 95%. This condition will affect the canopy interception loss value ranging from 24.83% up to 64.72%. The canopy cover and interception loss correlation denote that the interception loss amount reduces whilst the canopy cover increases.
Fertilizer nutrient requirements for corn are based on ex-pected yield and nutrient levels in the soil. This revision contains slight changes to the nitrogen (N) recommendation equation and the addition of cost adjustment and timing factors for the calculation of the recommended N rate. In place of a table for phosphorus (P) recommendations, a graph of recommended rates based on soil test P is presented. Nutrient Needs Crop production in Nebraska typically requires (N) fer-tilization to supplement what is available from the soil. After N, phosphorus (P) is the nutrient most likely to be defi cient for profi table corn production. For corn after corn, annually test for residual nitrate in the soil profi le in spring (0 -4 feet) to fi ne-tune your N recom-mendation. Soil nitrate sampling is generally not needed for corn grown after soybean unless the fi elds have a recent manure history. To determine P, potassium (K) and micronutrient needs and the level of soil organic matter, collect soil samples from a depth of 0 -8 inches every three to fi ve years in the fall. Most Nebraska soils supply adequate amounts of K, sulfur, zinc, and iron, but on some soils the corn crop will benefi t from applying one or more of these nutrients. Calcium, magnesium, boron, chlorine, copper, manganese, and molybdenum are seldom, if ever, defi cient for corn production in Nebraska. The complete University of Nebraska nutrient recommendations for all crops are available at soiltest.unl.edu.
Canola (Brassica napus L.) has potential to be grown as a dryland crop to diversify the winter wheat (Triticum aestivum L.)-fallow production system of the semiarid central Great Plains. Extensive regional field studies have not been conducted under rainfed conditions to provide farmers, agricultural lenders, and crop insurance providers with information about the production potential and expected yield variability of canola in this region. The purpose of this study was to use an agricultural system model to simulate canola production under rainfed conditions in the central Great Plains and to determine the economic viability of canola production. The CROPGRO-canola model was used within the Root Zone Water Quality Model (RZWQM2) with weather data (1993-2008) to simulate canola yield for nine central Great Plains locations under four plant-available water (PAW) contents at planting. Average yield with 75% PAW was highest (1725 kg ha(-1)) at Champion, NE, in the north-central area and lowest (975 kg ha(-1)) at Walsh, CO, in the south-central area. Simulated yields increased with increasing PAW at planting at an average rate of 5.31 kg ha(-1) mm(-1). Yield variability was simulated to be lowest at Sidney, NE, Stratton, CO, and Walsh, CO, and highest at Akron, CO, Tribune, KS, and Garden City, KS. Yield variability did not consistently change with amount of PAW across the region. Calculated average net returns indicate that profitable canola production is possible across a large portion of the central Great Plains when PAW at planting is at least 50%.
The first crop chosen to parameterize and test the new FAO AquaCrop model is maize (Zea may.; L.). Working mainly with data sets from 6 yr of maize field experiments at Davis, CA, plus another 4 yr of Davis maize canopy data, a set of conservative (nearly constant) parameters of AquaCrop, presumably applicable to widely different conditions and not specific to a given crop cultivar, was evaluated by test simulations, and used to simulate the 6 yr of Davis data. The treatment variable was irrigation-withholding water after planting continuously, only up to tasseling, from tasseling onward, or intermittently, and with full irrigation (FI) as the control. From year to year, plant density (7-11.9 plants m(-2)), planting date (14 May-15 June), cultivar (a total of four), and atmospheric evaporative demand varied. The conservative parameters included: canopy growth and canopy decline coefficient (CDC); crop coefficient for transpiration (Tr) at full canopy; normalized water productivity for biomass (WP*); soil water depletion thresholds for the inhibition leaf growth and of stomatal conductance, and for the acceleration of canopy senescence; reference harvest index (HI(o)); and coefficients for adjusting harvest index (HI) in relation to inhibition of leaf growth and of stomatal conductance. With all 19 parameters held constant, AquaCrop simulated the final aboveground biomass within 10% of the measured value for at least 8 of the 13 treatments (6 yr of experiments) and also the grain yield for at least five of the cases. In at least four of the cases, the simulated results were within 5% of the measured for biomass as well as for grain yield. The largest deviation between the simulated and measured values was 22% for biomass, and 24% for grain yield. Importantly, the simulated pattern of canopy progression and biomass accumulation over time were close to those measured, with Willmott's index of agreement (d) for 11 of the 13 cases being >= 0.98 for canopy cover (CC), and >= 0.97 for biomass. Accelerated senescence of canopy due to water stress, however, proved to be difficult to simulate accurately; of the six cases, the index of agreement for the worst one was 0.957 for canopy and 0.915 for biomass. Possible reasons for the discrepancies between the simulated and measured results include simplifications in the model and inaccuracies in measurements. The usefulness of AquaCrop with well-calibrated conservative parameters in assessing water use efficiency (WUE) of a crops under different conditions and in devising strategies to improve WUE is discussed.
Predicting yield is increasingly important to optimize irrigation under limited available water for enhanced sustainability and profitable production. Food and Agriculture Organization (FAO) of the United Nations addresses this need by providing a yield response to water simulation model (AquaCrop) with limited sophistication. In this study, AquaCrop was parameterized and tested for cotton (Gossypium hirsutum L.) under full (100%) and deficit (40, 60, and 80% of full) irrigation regimes in the hot, dry, and windy Mediterranean environment of northern Syria. Model parameterization used the 2006 data and was straightforward within the designed user-interface, owing to the limited number of key parameters. Accurate simulation of canopy cover was central to sound prediction of evapotranspiration and biomass accumulation. Key user-input parameters for this purpose were identified as the coefficients defining canopy development and the threshold soil water depletion levels for the water stress indices. The parameterized model was tested using data from the 2004 and 2005 seasons, resulting in accurate prediction of evapotranspiration (<13% error). The predicted yield values were within 10% of measurements, except in the 60 and 80% irrigation regimes in 2004, with errors up to 32%. The model closely predicted the trend in total soil water, but deviation existed for individual soil layers. This study provides first estimate values for cotton parameters useful for future model testing and use. Model parameterization is site-specific, and thus the applicability of key calibrated parameters must to be tested under different climate, soil, variety, irrigation methods, and field management.
In a 2‐yr multiple‐site field study conducted in western Nebraska during 1999 and 2000, optimum dryland corn ( Zea mays L.) population varied from less than 1.7 to more than 5.6 plants m ⁻² , depending largely on available water resources. The objective of this study was to use a modeling approach to investigate corn population recommendations for a wide range of seasonal variation. A corn growth simulation model (APSIM‐maize) was coupled to long‐term sequences of historical climatic data from western Nebraska to provide probabilistic estimates of dryland yield for a range of corn populations. Simulated populations ranged from 2 to 5 plants m ⁻² . Simulations began with one of three levels of available soil water at planting, either 80, 160, or 240 mm in the surface 1.5 m of a loam soil. Gross margins were maximized at 3 plants m ⁻² when starting available water was 160 or 240 mm, and the expected probability of a financial loss at this population was reduced from about 10% at 160 mm to 0% at 240 mm. When starting available water was 80 mm, average gross margins were less than $15 ha ⁻¹ , and risk of financial loss exceeded 40%. Median yields were greatest when starting available soil water was 240 mm. However, perhaps the greater benefit of additional soil water at planting was reduction in the risk of making a financial loss. Dryland corn growers in western Nebraska are advised to use a population of 3 plants m ⁻² as a base recommendation.
This article introduces the FAO crop model AquaCrop. It simulates attainable yields of major herbaceous crops as a function of water consumption under rainfed, supplemental, deficit, and full irrigation conditions. The growth engine of AquaCrop is water-driven, in that transpiration is calculated first and translated into biomass using a conservative, crop-specific parameter: the biomass water productivity, normalized for atmospheric evaporative demand and air CO, concentration. The normalization is to make AquaCrop applicable to diverse locations and seasons. Simulations are performed on thermal time, but can be on calendar time, in daily time-steps. The model uses canopy ground cover instead of leaf area index (LAI) as the basis to calculate transpiration and to separate out soil evaporation from transpiration. Crop yield is calculated as the product of biomass and harvest index (HI). At the start of yield formation period, HI increases linearly with time after a lag phase, until near physiological maturity. Other than for the yield, there is no biomass partitioning into the various organs. Crop responses to water deficits are simulated with four modifiers that are functions of fractional available soil water modulated by evaporative demand, based on the differential sensitivity to water stress of four key plant processes: canopy expansion, stomatal control of transpiration, canopy senescence, and HI. The HI can be modified negatively or positively, depending on stress level, timing, and canopy duration. AquaCrop uses a relatively small number of parameters (explicit and mostly intuitive) and attempts to balance simplicity, accuracy, and robustness. The model is aimed mainly at practitioner-type end-users such as those working for extension services, consulting engineers, governmental agencies, nongovernmental organizations, and various kinds of farmers associations. It is also designed to fit the need of economists and policy specialists who use simple models for planning and scenario analysis.
The AquaCrop model was developed to replace the former FAO I&D Paper 33 procedures for the estimation of crop productivity in relation to water supply and agronomic management in a framework based on current plant physiological and soil water budgeting concepts. This paper presents the software of AquaCrop for which the concepts and underlying principles are described in the companion paper (Steduto et al., 2009). Input consists of weather data, crop characteristics, and soil and management characteristics that define the environment in which the crop will develop. Algorithms and calculation procedures modeling the infiltration of water, the drainage out of the root zone, the canopy and root zone development, the evaporation and transpiration rate, the biomass production, and the yield formation are presented. The mechanisms of crop response to cope with water shortage are described by only a few parameters, making the underlying processes more transparent to the user. AquaCrop is a menu-driven program with a well-developed user interface. With the help of graphs which are updated each time step (1 d) during the simulation run, the user can track changes in soil water content, and the corresponding changes in crop development, soil evaporation and transpiration rate, biomass production, and yield development. One can halt the simulation at each time step, to study the effect of changes in water related inputs, making the model particularly suitable for developing deficit irrigation strategies and scenario analysis.
Dryland cropping systems research in the semiarid Great Plains region requires a substantial investment in land, labor, and other resources. The objective of this analysis was to illustrate that crop simulation models can assist scientists in making more efficient use of these resources by providing insight on potential plant responses to alterations in cropping systems before conducting field research. Models included in DSSAT 3.5 were used to simulate two cropping systems studies that evaluated the inclusion of grain sorghum [Sorghum bicolor (L.) Moench] into a traditional wheat (Triticum aestivum L.)-fallow system in western Kansas and soybean [Glycine max (L.) Merr.] into continuous grain sorghum in north-central Kansas. CERES-Wheat overestimated wheat yields by 16% although no consistent reason was identified for these errors. The model also simulated complete plant stand losses from winter injury in 5 yr when no stand losses were observed. CERES-Sorghum underestimated grain sorghum yields by approximately 27% across both studies. Overestimating the impact of water stress on plant growth appeared to be common at the western site, and a lack of response to N when grown in rotation with soybean appeared to be the primary sources of error at the northern site. Using uniform genetic coefficients to span a 19-yr study also contributed to errors in simulating sorghum yields. CROPGRO simulated soybean within 20% and closely mimicked annual responses of soybean yields to weather patterns. If researchers used these results to evaluate the objectives of both studies before conducting fieldwork, despite the errors, the overall trends would have been similar to those measured in the Field. These results would have also enabled researchers to focus their research efforts, thus more efficiently using their resources.