![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
Agronomy Journal
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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... This device measures the canopy cover at multiple angles, and uses the assumption of uniformly randomly distributed leaves to calculate LAI. Canopy cover and LAI are highly correlated metrics [84], so these are considered together for this review. ...
Agricultural research is essential for increasing food production to meet the needs of a rapidly growing human population. Collecting large quantities of agricultural data helps to improve decision making for better food security at various levels: from international trade and policy decisions, down to individual farmers. At the same time, deep learning has seen a wave of popularity across many different research areas and data modalities. And satellite imagery has become available in unprecedented quantities, driving much research from the wider remote sensing community. The data hungry nature of deep learning models and this huge data volume seem like a perfect match. But has deep learning been adopted for agricultural tasks using satellite images? This systematic review of 193 studies analyses the tasks that have reaped benefits from deep learning algorithms, and those that have not. It was found that while Land Use / Land Cover research has embraced deep learning algorithms, research on other agricultural tasks has not. This poor adoption appears to be due to a critical lack of labelled datasets for these other tasks. Thus, we give suggestions for collecting larger datasets. Additionally, satellite images differ from ground-based images in a number of ways, resulting in a proliferation of interesting data interpretations unique to satellite images. So, this review also introduces a taxonomy of data input shapes and how they are interpreted in order to facilitate easier communication of algorithm types and enable quantitative analysis.
... То је својство које се односи на пропорционалну површину земљишта коју покрива вертикална пројекција крошње (Jennings et al., 1999), у овом случају листова. Он има експоненцијалну везу са индексом лисне површине (Hsiao et al., 2009;Farahani et al., 2009;Nielsen et al., 2012), који представља укупну једнострану површину зеленог лишћа по јединици површине земљишта (Fang and Liang, 2014). Због њихове експоненцијалне везе (R 2 = 0,971, Nielsen et al., 2012) може се лако одредити једна или друга особина, у зависности шта имамо одређено. ...
Kukuruz (Zea Mays L.) zauzima centralno mesto u ishrani životinja, u nerazvijenim krajevima u ishrani ljudi, a koristi se i kao sirovina za industrijsky preradu. Proizvođači često primenjuju veće količine azota i gušći sklop biljaka od optimalnog, jer ove dve agrotehničke mere najviše utiču na povećanje prinosa zrna po jedinici površine. Međutim, takvi postupci često dovode do poleganja, što može značajno smanjiti prinos i kvalitet zrna. Cilj istraživanja je bio da se utvrdi da li tretiranje kukuruza etefonom menja morfološke osobine nadzemnog dela biljke i na taj način smanjuje negativne efekte većih gustina setve i količina azota na poleganje, kao i da se odredi optimalna doza etefona za smanjenje poleganja uz povećanje prinosa. Dva poljska ogleda su izvedena tokom 2021-2022. godine u AP Vojvodini, Srbija, koristeći split-plot dizajn sa slučajnim rasporedom tretmana u 4 ponavljanja. Ispitivane su različite doze etefona (0, 280, 560, 840 g ha-1), gustine setve (65.000, 75.000, 85.000 biljaka ha-1) i doze azota (0, 150, 250 kg ha-1) na morfološke osobine, prinos zrna i poleganje tri hibrida kukuruza (NS 3022, NS 4000, NS 444 Ultra). Rezultati su pokazali da je primena etefona značajno smanjila poleganje biljaka u uslovima povoljnim za poleganje kod sva tri hibrida, dok gustina setve i doza azota nije imala značajan uticaj na poleganje. Suva i specifična masa internodije i odnos visine do klipa i visine biljke su najznačajnije morfološke osobine čijom optimizacijom možemo smanjiti poleganje. Najveći prinos zrna kod hibrida NS 3022 i NS 4000 postignut je pri dozi etefona od 280 g ha-1 u prvoj godini, dok su optimalne doze varirale u drugoj godini, u zavisnosti od hibrida. Kod hibrida NS 444 Ultra nije došlo do značajnog povećanja prinosa nakon primene etefona. U proseku, najveći teorijski prinos zrna uz smanjenje poleganja za 26% ostvaren je na gustini setve od 65.000 biljaka ha-1 uz dozu etefona od 407 g ha-1. Istraživanje je pokazalo da etefon može ublažiti negativne efekte većih gustina i doza azota, što ga čini potencijalnim rešenjem za smanjenje poleganja u proizvodnji kukuruza. S obzirom na klimatske promene, pojavu olujnih vetrova i povećan rizik od poleganja biljaka, primena etefona bi mogla biti efikasan tehnološki pristup za poboljšanje stabilnosti proizvodnje kukuruza.
Maize (Zea mays L.) holds a central role in animal feed, human diets in underdeveloped regions, and as a raw material for industrial processing. Producers often apply larger amounts of nitrogen and higher planting density than optimal, because these two agronomic practices have the most significant impact on increasing grain yield per unit area. However, such practices often lead to lodging, which can significantly reduce grain yield and quality. The aim of this research was to determine whether treating maize with ethephon alters the morphological characteristics of the above-ground part of the plant and thereby reduces the negative effects of higher planting densities and nitrogen amounts on lodging, as well as to determine the optimal dose of ethephon for reducing lodging while increasing yield. Two field trials were conducted during 2021-2022 in the AP Vojvodina, Serbia, using a split-plot design with randomly arranged treatments in 4 replications. Different doses of ethephon (0, 280, 560, 840 g ha-1), planting densities (65,000, 75,000, 85,000 plants ha-1), and nitrogen doses (0, 150, 250 kg ha-1) were examined for their effects on morphological characteristics, grain yield, and lodging of three maize hybrids (NS 3022, NS 4000, NS 444 Ultra). The results showed that the application of ethephon significantly reduced plant lodging under favorable lodging conditions for all three hybrids, while planting density and nitrogen dose had no significant impact on lodging. Dry and specific internode mass and relation between ear height and plant height are the most important morphological characteristics that can be optimized to reduce lodging. The highest grain yield of hybrids NS 3022 and NS 4000 was achieved with an ethephon dose of 280 g ha-1 in the first year, while optimal doses varied in the second year depending on the hybrid. No significant yield increase after ethephon application was recorded for NS 444 Ultra. On average, the highest theoretical grain yield along with 26% lodging reduction compared to control was achieved at the planting density of 65,000 plants ha-1 with the ethephon dose of 407 g ha-1. The research indicated that ethephon can mitigate the negative effects of higher densities and nitrogen doses, making it a potential solution for reducing lodging in maize production. In view of climate change, the occurrence of stormy winds, and the increased risk of plant lodging, the application of ethephon could be an effective technological approach in improving the stability of maize production.
... Previous studies have found a relationship between the LAI and CC for different crops. Nielsen et al. [21] determined an exponential relationship between the LAI and CC for corn (R 2 = 0.97), winter wheat (R 2 = 0.96), and spring triticale (R 2 = 0.90) under dryland and limited irrigation conditions. Córcoles et al. [22] analyzed the relationship between LAI and CC in onion, fitting a linear (R 2 = 0.84), a polynomial (R 2 = 0.84), and an exponential model (R 2 = 0.75), obtaining better results with the linear and polynomial models. ...
In arid and semiarid regions, crop production has high irrigation water demands due to low precipitation. Efficient irrigation water management strategies can be developed using crop growth models to assess the effect of different irrigation management practices on crop productivity. The leaf area index (LAI) is an important growth parameter used in crop modeling. Measuring LAI requires specialized and expensive equipment not readily available for producers. Canopy cover (CC) and canopy height (CH) measurements, on the other hand, can be obtained with little effort using mobile devices and a ruler, respectively. The objective of this study was to determine the relationships between LAI, CC, and CH for fully and deficit-irrigated alfalfa (Medicago sativa L.). The LAI, CC, and CH measurements were obtained from an experiment conducted at the Valley Road Field Lab in Reno, Nevada, starting in the Fall of 2020. Three irrigation treatments were applied to two alfalfa varieties (Ladak II and Stratica): 100%, 80%, and 60% of full irrigation demands. Biweekly measurements of CC, CH, and LAI were collected during the growing seasons of 2021 and 2022. The dataset was randomly split into training and testing subsets. For the training subset, an exponential model and a simple linear regression (SLR) model were used to determine the individual relationship of CC and CH with LAI, respectively. Also, a multiple linear regression (MLR) model was implemented for the estimation of LAI with CC and CH as its predictors. The exponential model was fitted with a residual standard error (RSE) and coefficient of determination (R2) of 0.97 and 0.86, respectively. A lower performance was obtained for the SLR model (RSE = 1.03, R2 = 0.81). The MLR model (RSE = 0.82, R2 = 0.88) improved the performance achieved by the exponential and SLR models. The results of the testing indicated that the MLR performed better (RSE = 0.82, R2 = 0.88) than the exponential model (RSE = 0.97, R2 = 0.86) and the SLR model (RSE = 1.03, R2 = 0.82) in the estimation of LAI. The relationships obtained can be useful to estimate LAI when CC, CH, or both predictors are available and assist with the validation of data generated by crop growth models.
... There was also a vertical distribution of the tree canopy layers and a dense horizontal spread of the tree canopy throughout S2. The size of the canopy cover is directly related to the leaf area index (LAI), where a dense canopy cover conveys a positive relationship with an increase in LAI values [29,30]. It is also an indicator of photosynthesis capabilities, where the exchange of water vapor and oxygen is a by-product of photosynthesis [31]. ...
Urban green spaces are crucial for the exchange of energy fluxes, particularly sensible heat (QH) and latent heat (QE) fluxes. Therefore, this study aimed to investigate the characteristics of plant communities in urban green areas that affect turbulent fluxes, specifically QH and QE. The energy balance was measured using an eddy covariance system tower set up in three green areas at Kasetsart University: the Varunawan Garden (S1), the 100-Year Garden of Luang Suwan Vajokkasikit (S2), and the Phaholyothin Garden (S3). The results show that the canopy coverages of trees in S1, S2, and S3 were 526.23, 895.81, and 756.70 m2, respectively. The Bowen ratios (QH/QE) during the daytime in S1, S2, and S3 were 1.75, 1.09, and 1.43, respectively. These relationships suggest that dense trees, a dense canopy layer top, and the presence of water sources within the green areas resulted in a higher latent heat flux and a lower proportion of sensible heat flux. The findings of this study can be used as a guideline for the development and improvement of plant community structures in green areas within urban climate change adaptation.
... Canopy cover refers to the proportion of land covered by the vertical projection of the vegetation canopy (Guevara-Escobar et al., 2005;Lee and Lee, 2011), which can be used to quantify the expansion of the rice canopy in the horizontal dimension as AGB increases. Studies have shown that CC is a reliable parameter for reflecting plant canopy growth and estimating AGB, Leaf Area Index (LAI), and yield (Nielsen et al., 2012;Goodwin et al., 2018;Garcıá-Martıńez et al., 2020). Currently, there are relatively few studies combining PH and CC for AGB estimation, emphasizing the need to strengthen the role of CC. ...
Introduction
Unmanned aerial vehicles (UAVs) equipped with visible and multispectral cameras provide reliable and efficient methods for remote crop monitoring and above-ground biomass (AGB) estimation in rice fields. However, existing research predominantly focuses on AGB estimation based on canopy spectral features or by incorporating plant height (PH) as a parameter. Insufficient consideration has been given to the spatial structure and the phenological stages of rice in these studies. In this study, a novel method was introduced by fully considering the three-dimensional growth dynamics of rice, integrating both horizontal (canopy cover, CC) and vertical (PH) aspects of canopy development, and accounting for the growing days of rice.
Methods
To investigate the synergistic effects of combining spectral, spatial and temporal parameters, both small-scale plot experiments and large-scale field testing were conducted in Jiangsu Province, China from 2021 to 2022. Twenty vegetation indices (VIs) were used as spectral features, PH and CC as spatial parameters, and days after transplanting (DAT) as a temporal parameter. AGB estimation models were built with five regression methods (MSR, ENet, PLSR, RF and SVR), using the derived data from six feature combinations (VIs, PH+CC, PH+CC+DAT, VIs+PH +CC, VIs+DAT, VIs+PH+CC+DAT).
Results
The results showed a strong correlation between extracted and ground-measured PH (R2 = 0.89, RMSE=5.08 cm). Furthermore, VIs, PH and CC exhibit strong correlations with AGB during the mid-tillering to flowering stages. The optimal AGB estimation results during the mid-tillering to flowering stages on plot data were from the PLSR model with VIs and DAT as inputs (R ² = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R ² = 0.88, RMSE=1131 kg/ha, NRMSE=9.94%). For the field sampling data, the ENet model combined with different feature inputs had the best estimation results (%error=0.6%–13.5%), demonstrating excellent practical applicability.
Discussion
Model evaluation and feature importance ranking demonstrated that augmenting VIs with temporal and spatial parameters significantly enhanced the AGB estimation accuracy. In summary, the fusion of spectral and spatio-temporal features enhanced the actual physical significance of the AGB estimation models and showed great potential for accurate rice AGB estimation during the main phenological stages.
... One of the distinctive features that distinguish the AquaCrop from other crop models is the explanation of plant growth with canopy cover instead of leaf area index (Nielsen et al., 2012). Several researches have been conducted to calibrate the AquaCrop model to simulate the canopy cover of different crops. ...
The study aims to determine the vegetation covers (natural and cultivated) affected by precipitation amounts in the Syrian coastal area from 2000 to 2019, and for this, precipitation data for 23 climatic stations were used, in addition to the spectral vegetation indices EVI and LAI from the MODIS satellite. The results showed that vegetation covers are affected by the amount of precipitation, but there is a difference in vulnerability degree in the cultivated and natural cover (forests). The cultivated vegetation covers were more affected by the decrease or increase in the precipitation amount, as the area is coastal and depends heavily on rainwater. The cultivated cover has declined significantly in the years with low precipitation, especially the years, while it responded well to the high precipitation values in 2003, 2012, 2015 and 2019. While the forests showed a slight impact on the decrease in the amount of precipitation due to their nature of adaptation to the conditions of the region, where most of the years maintained stable and high values for both EVI and LAI indices, except for 2014 and 2016 which were the years with the sharp decline in the amount of precipitation. When the forests were affected by the decline in precipitation in addition to the fires resulting from the crisis conditions in Syria, which increased the severity of damage in forest cover. This indicates that the forests were affected in the first place as a result of fires and in the second place as a result of precipitation, while the cultivated cover was mainly affected by the lack of precipitation.
Sensor data and agro-hydrological modeling have been combined to improve irrigation management. Crop water models simulating crop growth and production in response to the soil-water environment need to be parsimonious in terms of structure, inputs and parameters to be applied in data scarce regions. Irrigation management using soil moisture sensors requires them to be site-calibrated, low-cost, and maintainable. Therefore, there is a need for parsimonious crop modeling combined with low-cost soil moisture sensing without losing predictive capability.
This study calibrated the low-cost capacitance-based Spectrum Inc. SM100 soil moisture sensor using multiple least squares and machine learning models, with both laboratory and field data. The best calibration technique, field-based piece-wise linear regression (calibration r2 = 0.76, RMSE = 3.13 %, validation r2 = 0.67, RMSE = 4.57 %), was used to study the effect of sensor calibration on the performance of the FAO AquaCrop Open Source (AquaCrop-OS) model by calibrating its soil hydraulic parameters.
This approach was tested during the wheat cropping season in 2018, in Kanpur (India), in the Indo-Gangetic plains, resulting in some best practices regarding sensor calibration being recommended. The soil moisture sensor was calibrated best in field conditions against a secondary standard sensor (UGT GmbH. SMT100) taken as a reference (r2 = 0.67, RMSE = 4.57 %), followed by laboratory calibration against gravimetric soil moisture using the dry-down (r2 = 0.66, RMSE = 5.26 %) and wet-up curves respectively (r2 = 0.62, RMSE = 6.29 %). Moreover, model overfitting with machine learning algorithms led to poor field validation performance. The soil moisture simulation of AquaCrop-OS improved significantly by incorporating raw reference sensor and calibrated low-cost sensor data. There were non-significant impacts on biomass simulation, but water productivity improved significantly. Notably, using raw low-cost sensor data to calibrate AquaCrop led to poorer performances than using the literature. Hence using literature values could save sensor costs without compromising model performance if sensor calibration was not possible. The results suggest the essentiality of calibrating low-cost soil moisture sensors for crop modeling calibration to improve crop water productivity.
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 mays 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 (HIo); 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.
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 CO2 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.
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.
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.
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.
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.