FAO AquaCrop Research Articles

Parameterizing the FAO AquaCrop Model for Rainfed and Irrigated Field‐Grown Sweet Potato

Crop production in the Caribbean is dominated by small open field holdings that are almost totally reliant on rainfall. Sweet potato (Ipomoea batatas L. Lam. [Convolvulaceae]) has been identified as an important commodity to attain food and nutrition security goals of the region, particularly in light of a changing climate. The crop has high nutritional value, innate drought‐tolerant properties, and can be grown with relatively low inputs. The routine use of crop models for yield optimization is largely absent in the Caribbean. In this study, an attempt was made to parameterize the FAO AquaCrop model for sweet potato for the first time. AquaCrop is a simulation model for crop water productivity, designed primarily for use in irrigation management. Parameters were developed using data from three sweet potato cultivars grown in two agroecological zones in Jamaica under rainfed and irrigated conditions. Digital photography was combined with an automated canopy estimator to track canopy development, and sample harvesting was done throughout the crop season. The overall simulation of biomass was good, with deviations of <28% for four out of six simulations, and season‐long performance of the model was commendable. The simulation of yield presented more challenges, especially given the nonlinear rate of tuber development. The results, however, indicate that AquaCrop could be a useful tool for Caribbean agriculture in predicting the productivity of sweet potato under varying water availability.

Validating FAO AquaCrop using Landsat images and regional crop information

Defining crop response to water is a crucial part of decision-making for agricultural water management. This study is proposing an efficient and a low-cost approach to validate FAO's AquaCrop model using remote sensing (RS) estimates instead of crop ground measurements. A radiance use efficiency (RUE) based RS model for estimating aboveground biomass (AGB) is proposed with Landsat images and regional crop information. The RS estimates are used to validate AquaCrop's built-in crops and calibrate it under salinity stress. As a result, RS estimates of canopy cover (CC) and AGB were produced from an existing CC model and the proposed AGB model, respectively. These estimates became good replacements of the ground measurements for validation and calibration. Built-in maize of AquaCrop showed good agreement between simulations and RS estimates under non-stress conditions whereas built-in barley underestimated AGB compared to the RS estimates. By comparing the RS estimates in salinity-affected farms to AquaCrop simulations without considering salinity stress, AGB reduction due to salinity stress and corresponding CC reduction were quantified for calibration of AquaCrop under salinity stress. The results of calibration predicted initial soil salinity of saline-stressed farms and the values are within the possible ranges. The proposed methodology shows that the readily available Landsat images and regional crop information could extend the validation of built-in crops of AquaCrop to regions without ground measurements.

Prediction of climate change impacts on cotton yields in Greece under eight climatic models using the AquaCrop crop simulation model and discriminant function analysis

The impact of climate change on cotton yields in seven main arable crop sites in Greece (Agrinio, Alexandroupoli, Arta, Karditsa, Mikra, Pyrgos, Yliki) was investigated. The FAO AquaCrop (v.4) water driven model was used as a crop growth simulation tool under eight climatic models (HadRM3, C4I, REMO-MPI, ETHZ, CNRM, DMI-HIRHAM, KNMI, SMHI) based on IPPC's A1B emission scenario. The mean values of the models ensemble for temperature were +1.8°C until 2050 and +4°C until the end of the century. The respective values for precipitation were −11% and −24%. The research was applied over three periods, 1961–1990, 2021–2050 and 2071–2100. AquaCrop was calibrated for 2006 and validated for 2005 and 2007 using the field data from the experiments carried out in Karditsa (Central Greece). Root Mean Square Error for yield and biomass was 0.17 and 0.49t/ha, respectively, while Index of Agreement was 0.93 and 0.94. AquaCrop was run using the Growing Degree Day mode in order to account better for the temperature variations. However, it gave erratic results for some specific climatic models (SMHI, KNMI, CNRM) in some years within the period 1961–1990. A tendency towards increasing yields by the end of the century was detected for the majority of the climate models, especially in Western Greece (Arta, Agrinio, Pyrgos) and Northern Greece (Mikra, Alexandroupoli). The efficiency of the eight models for yield predictions in the seven sites was assessed by means of a discriminant function analysis. On the account of their function coefficients over the seven sites, it was found that the models DMI and C4I explained consistently a great proportion of variation among the three time periods whereas the models ETHZ, SMHI and KNMI were more efficient only in the periods 1961–1990, 2021–2050 and 2071–2099, respectively. By running the models DMI and C4I the relative impacts of climate change on seedcotton yield in the different areas were predicted and the results were discussed on the account of the corresponding changes in precipitation, temperature and crop evapotranspiration. These results will be useful for future irrigation planning in the study areas.

Application of the AquaCrop model to simulate the biomass of Miscanthus x giganteus under different nutrient supply conditions

AbstractThere are conflicting opinions about the need to fertilize Miscanthus and, also, the question has been raised whether Miscanthus should be irrigated, especially if water resources are limited. Crop growth modeling can help answer such questions. In this article the FAO AquaCrop water‐driven model was selected to simulate Miscanthus biomass under different nutrient and water supply conditions. The article reports the outcomes of 6‐year experiments with Miscanthus on two locations in Serbia: Zemun, where three fertilizer treatments were applied (Nl – 100 kg ha−1, Nopt 50 kg ha−1 and Nf nonfertilized), and Ralja, where only Nl 100 kg ha−1 was applied. Model calibration focused on the measured data (root depth, crop phenology, and the above‐ground biomass by year of growth. Calibration results showed a very good match between measured and simulated values. The largest and only significant difference was noted in 2008, when the crop was establishing and exhibited uneven radication. The simulation results for the next 5 years showed a variance from −4 to 5.7%, believed to be a very good match. A high coefficient of determination (R2 = 0.995) and high Willmott index of agreement (0.998) were also indicative of a good match between simulated and recorded biomass yields. The measured and simulated results for validated datasets at both locations were good. The average RMSE was 2.89 Mg ha−1; when compared to the deviations noted at the test site itself, it was apparent that they were smaller in all the years of research except the first year. The index of agreement was 0.97 and the coefficient of determination R2 0.947. The AquaCrop model can be used with a high degree of reliability in strategic planning of Miscanthus cultivation in new areas, under different nutrient and water supply and local weather and soil conditions.

Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focus on model parameterization

Several maize field experiments, including deficit and full irrigation, were performed in Ribatejo region, Portugal and were used to assess water stress impacts on yields using the AquaCrop model. The model was assessed after its parameterization using field observations relative to leaf area index (LAI), crop evapotranspiration, soil water content, biomass and final yield data and also using default parameters. LAI data were used to calibrate the canopy cover (CC) curve. Results showed that when the CC curve is properly calibrated, with root mean square errors (RMSE) smaller than 7.4%, model simulations, namely relative to crop evapotranspiration and its partition, show an improved accuracy. The model performance relative to soil water balance simulation revealed a bias in estimation but low estimation errors, with RMSE<13% of the total available soil water. However the model tends to overestimate transpiration and underestimate soil evaporation. A good model performance was obtained relative to biomass and yield predictions, with RMSE lower than 11% and 9% of the average observed biomass and yield, respectively. Overall results show adequacy of AquaCrop for estimating maize biomass and yield under deficit irrigation conditions, mainly when an appropriate parameterization is adopted. The model showed less good performance when using the default parameters but errors are likely acceptable when field data are not available.

Application and validation of AquaCrop model in simulating biomass and yield of oil flax in Northwest China

为了预测水分和养分对胡麻籽粒产量、生物量与水分生产率的影响, 使用FAO研发的水分驱动作物模型AquaCrop对胡麻在不同灌溉与氮磷水平下的生长情况进行模拟和验证。试验分别于2011年、2012年在甘肃省榆中县良种场进行, 试验设置4个灌溉水平, 3个氮水平, 3个磷水平。模型性能的评价采用模型效率(<i>E</i>)、决定系数(<i>R</i>²)、均方根误差(<i>RMSE</i>)和平均绝对误差(<i>MAE</i>)等统计指标。分析结果表明: AquaCrop模型校正的籽粒产量和生物量在不同灌溉与氮磷水平处理下的预测误差统计值为: 0.97<<i>E</i><0.99, 0.11<<i>RMSE</i><0.33, 0.11 t·hm^-2< MAE<0.42 t·hm^-2, 与2012年的试验观察数据(0.96<<i>E</i><0.99, 0.11<<i>RMSE</i><0.42, 0.11 t·hm^-2<<i>MAE</i><0.39 t·hm^-2)基本一致; 同时, 群体覆盖(<i>CC</i>)与生物量的模拟结果与测定值也非常拟合。AquaCrop模型在充分灌溉处理下预测胡麻产量, 比非充分灌溉处理下具更高的准确性。因而, 水分驱动模型AquaCrop在西北胡麻区不同的灌溉与田间管理措施下有较高的模拟精确性, 具有广阔的应用前景和价值。

Comparison of FAO Aquacrop and SWAP Agro-Hydrological Models to Simulate Water and Salt Transport during Growing Season of Winter Wheat

In this study, the performance of SWAP and FAO Aquacrop agro-hydrological models to predict water flow and salt transport in soil profile was evaluated under water and salt stresses during growing season of winter wheat. Field experiments were conducted with three levels of saline irrigation water including: S1, S2 and S3 corresponding to 1.4, 4.5 and 9.6 dSm -1 and four levels of irrigation depth including: I1, I2, I3 and I4 corresponding to 50, 75, 100 and 125 percent of crop water requirement with three replications for 2005-2006 period in Birjand region in Iran. The SWAP and Aquacrop models predicted the soil water content with an appropriate precision. The average normalized root mean square error (NRMSE) of calibration in soil water content prediction by SWAP and Aquacrop models, were calculated 4.81 and 18.91 %, respectively, and for validation, were calculated 7.34 and 19.6 %, respectively. The average normalized root mean square error (NRMSE) of calibration in ECe prediction by SWAP and Aquacrop models, were calculated 8.87 and 37.34 %, respectively, and for validation, were calculated 13.6 and 36.93 %, respectively. Results indicated that the Aquacrop model predicted ECe with more error compared with SWAP model. * Corresponding Author: Vahid Rezaverdinejad  verdinejad@gmail.com

Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe

This paper presents a procedure in which the water-driven water productivity model AquaCrop was fine-tuned and validated for maize for the local conditions in Zimbabwe and then applied to develop sowing management options for decision support. Data from experiments of 2 seasons in Harare and from 5 other sites around Zimbabwe were used for the local calibration and validation of AquaCrop. Model parameters such as the reference harvest index (HIo); the canopy growth coefficient (CGC); early canopy decline and normalised biomass water productivity (WPb*) were adjusted during model calibration. Model performance was satisfactory after calibration with a Nash-Sutcliffe model efficiency parameter (EF = 0.81), RMSE = 15% and R2 = 0.86 upon validation. To develop sowing guidelines, historical climate series from 13 meteorological stations around Zimbabwe were used to simulate maize yield for 6 consecutive sowing dates determined according to criteria applicable in Zimbabwe. Three varieties and typical shallow and deep soil types were considered in the simulation scenarios. The simulated yield was analysed by an optimisation procedure to select the optimum sowing time that maximised long-term mean yield. Results showed that highest yields depended on the climate of the site (rainfall availability), variety (length of growing cycle) and soil depth (soil water storage capacity). The late variety gave higher mean yields for all sowing dates in the maize belt. Staggered sowing is recommended as a way of combating the effects of rainfall variability and as an answer to labour constraints.Keywords: biomass water productivity, AquaCrop, maize sowing dates, crop modelling

Evaluation of the FAO AquaCrop model for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation

Winter wheat is the main crop on the North China Plain (NCP), and in this region the most limiting factor for the crop is water. The objective of this study was to adapt and test the ability of the FAO-developed AquaCrop model (v3.1) to simulate winter wheat grain yield, biomass, actual evapotranspiration (ETa) and total soil water content (0–120cm). Field experiments were conducted under deficit irrigation at the Luancheng Agro-ecosystem station (NCP) in 1998–2001, and the AquaCrop model was calibrated with treatment D (1999–2000); the rest of the data was used for validation of the model. The AquaCrop model was revalidated with data on measured grain yield from the experimental station for 1990–2010, considering actual field conditions. The second revalidation was done with the statistical grain yield for 1995–2010 in the study region. For the model validation, the significant differences between simulated and observed grain yield, biomass and ETa were in the order of: rainfed treatment>well-watered treatment>moderate water stress. Total soil water simulated by AquaCrop tends to follow closely the trend in the measured data, but with slight underestimations for irrigated treatments and significant overestimations for rainfed treatments. In general, errors in the model's evaluation such as RMSE and Willmot's d statistics were for grain yield (0.58Mgha−1, 0.92), biomass (0.87Mgha−1, 0.95), ETa (33.2mm, 0.93) and soil water content (24.5–37.6mm, 0.85–0.90). The overall results based on extensive validation and revalidation showed that AquaCrop is a valid model and can be used with a reliable degree of accuracy for optimizing winter wheat grain yield production and water requirement on the NCP.

Productivity, evapotranspiration, and water use efficiency of corn and tomato crops simulated by AquaCrop under contrasting water stress conditions in the Mediterranean region

The FAO AquaCrop model has been conceived as a tool for simulating, on a daily scale, the canopy cover (CC), biomass and the actual evapotranspiration and for simulating, on a seasonal scale, the final biomass, the harvested yield, the cumulate actual evapotranspiration, and the crop water use efficiency. This performance was analysed after a series of tests carried on 12 crop cycles, concerning corn and tomato grown in the Mediterranean region, and having three levels of plant water stress: absence of plant water stress (control), moderately stressed and severely stressed. The results highlight the effects of three factors affecting the AquaCrop performances: the species, the level of plant water stress during the crop cycle and the output variable to simulate.The AquaCrop adequately simulates the daily canopy cover (CC) in control treatments of tomato and corn, and in moderate stress treatment of corn. In the severe stressed treatment of corn, the simulated values of CC were close to the measured values only from sowing to 60 days after sowing, after that the simulated values do not fit the measurements.The AquaCrop model adequately simulates the daily biomass accumulation under all treatments in tomato and under non-stressed and moderate stressed treatments in corn. However, the simulated biomass outputs were generally overestimated during the late stages of the crop cycles and, consequently, the yield also exhibited a tendency to be overestimated. Nevertheless, the yield overestimation can be retained as acceptable because the normalised differences (D) between the simulations and measured values were less than 15% on average. An exception was the tomato yield simulated in the severely stressed treatment, for which D was greater than 30%. In contrast, in the case of the severely stressed treatment in corn, AquaCrop did not exhibit any aptitude for simulating the biomass or the grain yield. In fact, the model predicts the absence of any yield production, while 5 t ha−1 of grain were actually measured in the severely stressed treatment.The daily actual evapotranspiration simulated by AquaCrop was consistent with the observations only in the case of the control treatments of tomato, in all the three seasons. In contrast, for the other treatments (all treatments in corn and all stressed treatments in tomato), the quality of the evapotranspiration simulation was poor.In general, AquaCrop underestimated the seasonal values of evapotranspiration. The normalised differences between the seasonal values of the observations and simulations are acceptable in the case of the tomato evapotranspiration (D=−7%). However, in the case of corn, the differences are related to the level of plant water stress, and they become unacceptable (D=−36%) in the severely stressed treatments.The overestimation of the yield and the underestimation of the seasonal evapotranspiration cause the simulations of the water use efficiency to be overestimated. In the specific case of corn, due to the unacceptable performance of the model under severely stressed treatments, the linear regression between the observations and measurements of water use efficiency is unsatisfactory.The potential uses of the AquaCrop model as a tool for research purposes aimed to enhance the water efficiency and as a tool for managing irrigation have been deeply discussed. The paths that should be followed in the future to improve the model simulations have also been suggested

Performance assessment of the FAO AquaCrop model for irrigated cabbage on farmer plots in a semi-arid environment

Small-scale irrigation initiatives are expanding rapidly in Burkina Faso. However, in many cases optimal yields are not being obtained despite the available water and the required nutrient applications. Local stakeholders need an easy-to-use decision-support tool to assess irrigation water use and its impact on yield. In this study, a water-driven crop model, AquaCrop, developed by FAO, was adapted for cabbage (Brassica oleracea L.) using a limited dataset and leave-one-out cross-validation (LOOCV). The experiment was conducted in south-western Burkina Faso on small irrigated farmer plots, where optimal managerial conditions could not always be guaranteed. Statistical indicators – normalized root mean square error (nRMSE) and index of agreement (d) – suggested that the model is very reliable for simulating cabbage biomass yield and soil water content (low nRMSE and d-index near 1). The relationship between observed and simulated yield produced a d-index of 0.99 and an nRMSE of 1.39% (or 0.59ton/ha). The comparison between observed and modeled soil water content gave a d-index of 0.90 and an nRMSE of 4.38% (or 9.13mm). Also of interest was the indirect link between plant density and yield via maximum canopy cover, which can considerably simplify yield estimation. It was concluded that AquaCrop was a very useful tool for enabling local end-users to evaluate and optimize cabbage yield and irrigation water use.

Cereal yield stabilization in Terai (Nepal) by water and soil fertility management modeling

Rice (Oryza sativa L.), maize (Zea mays L.) and wheat (Triticum aestivum L.) are the major cereals cultivated in the Terai region of Nepal. In the region, agriculture is mainly rainfed and fertilizer applications are low. In this study, water and soil fertility management was modeled with the FAO AquaCrop model. For the model calibration and validation, field trials were set up during two years in Rampur, a representative area of Terai at Chitwan. The statistical comparison between the simulated and observed yield of the validation fields resulted in an average RRMSE (relative root mean squared error), R2 (coefficient of determination) and EF (Nash–Sutcliffe efficiency) of 0.05, 0.94 and 0.90 for rice, 0.08, 0.90 and 0.87 for wheat and 0.08, 0.97 and 0.96 for maize, respectively. The validated model was subsequently used to simulate different scenarios with 30 years of historical climatic data to determine realistic soil fertility and water management strategies for increasing crop yields. For the monsoon crops (rice and maize) that benefit from abundant rainfall, only an improved fertility management could increase crop yields by up to 65% and 58%, for rice and maize, respectively. During the winter and spring, seasons with lesser rainfall, crops were severely under-watered in absence of irrigation. For winter wheat, application of deficit irrigation increased yield by up to 67–197% compared to the rainfed crop yield for different fertility levels. For spring maize, full irrigation with increased fertilizer application was recommended to increase the yield. However, by considering the water scarcity in the region, application of deficit irrigation resulted in good and stable yields under low to moderate fertility and always resulted in a better water productivity than fully irrigated crops. It was demonstrated that increasing soil fertility of the winter and spring crops without considering irrigation could sometimes result in crop failure, instead of a yield increase. For fertilizer applications above 50% of the national recommended fertilizer applications, full irrigations are recommended for best yield results for winter and spring crops.

Validating the FAO AquaCrop Model for Rainfed Maize in Pennsylvania

It is widely known that a close relationship exists between crop production and water stress. In this study, field‐measured data were used to test the performance of AquaCrop and its ability to capture this relationship for rainfed maize (Zea mays L.) in Pennsylvania. The objectives were to evaluate AquaCrop’s ability to simulate the progression of cumulative biomass and grain yield with time, final biomass and harvestable yield, and volumetric water content at six depths. Two years of data from a study conducted in Rock Springs, PA, were used to validate AquaCrop’s ability to accurately simulate the progression of cumulative biomass and grain yield with time, as well as final biomass and harvestable yield. Data collected from January 2004 to March 2006 from a study conducted near Landisville, PA, were used to assess AquaCrop’s ability to effectively simulate soil moisture content at six depths. In addition, the 2004 and 2005 seasonal final biomass measurements obtained from the Landisville location were compared with the model’s simulated values. The results indicated that AquaCrop was able to accurately simulate the progression of cumulative biomass and grain yield with time, with index of agreement values ranging from 0.96 to 0.99. Comparisons between simulated and measured final biomass and final harvestable yield produced biomass deviations ranging from 2.4 to 20.7% and yield deviations of 2.9 and 15.3%. The water balance evaluation indicated that, averaged across all depths, the results were consistent with other validation studies of soil water balance models, with RMSE ranging from 1.5 to 9.8% (v/v).

Effect of climate change on rice yield at Kharagpur, West Bengal

The daily rainfall and temperature data (1977-2007) of Kharagpur were analyzed to know seasonal and annual variability. The monsoon season June-September was characterized for seasonal trends. FAO Aquacrop model version 3.1 was tested to understand the response of rice to temperature, CO2, water and to simulate the grain yield of rice under various independent data sets. The inter-annual variability and trends of monthly maximum, minimum, mean temperatures and rainfall for the crop growing season had been analyzed. The results showed that there was decrease in yield with per°C increase in temperature and increase in yield with per mm increase in rainfall in Subtropical region. The results of the study revealed that the grain yield increased an average of 0.35 kg/ha with per mm increase in rainfall and decreased by 156.2 kg/ha per degree rise in mean temperature at that region. The simulation study had also conducted under different temperature, rainfall scenarios and CO2 concentration. The most negative scenario was Tmax4°C+Tmin4°C+50 ppm CO2 (grain yield reduced by 8.31%) and Tmax4°C+Tmin4°C+(P-10)mm (grain yield reduced by 37.67%). Keywords - Aquacrop v3.1, Climate variability, temperature, trend, rainfall, yield

To tie or not to tie ridges for water conservation in Rift Valley drylands of Ethiopia

The Rift Valley drylands of Ethiopia are characterized by sandy loam soils that have poor fertility and unreliable rainfall conditions. The aim of this study was to examine the potential benefit of rainwater harvesting by tied-ridges and improved soil fertility on maize productivity through field experimentation and simulation with the FAO's AquaCrop model. The effect of tied-ridges with and without manure on maize yield at smallholder farms was studied during the years 2009 (very dry, 96% probability of exceedance) and 2010 (normal year, 46% probability of exceedance). During a normal rainfall, the mean yield of maize grain was 4.0Mgha−1 when tied-ridges were applied in combination with manure (at 4.5Mgha−1 rate); 3.4Mgha−1 when tied-ridges were applied without manure; 3.5Mgha−1 when traditional tillage was applied in combination with manure; and 2.7Mgha−1 when traditional tillage was applied without manure. Long-term simulations with the FAO's AquaCrop showed that the root zone soil water may exceed field capacity for consecutive days during above average rainfall seasons in the shallow sandy loams. The question thus is when to tie or not to tie ridges. Therefore, upon proper calibration of the FAO's AquaCrop model, the effect of tied-ridges and improved soil fertility was simulated in response to different amounts of seasonal rainfall, number of rainfall days and sowing times. Simulations revealed that, during below average rainfall seasons (280–330mm), tied-ridges are more effective at improving crop yields than enhancing the fertility level of the soil. But during above average rainfall seasons, the rainwater that is held in tied-ridges can be more effectively utilised when the current fertility level of the soil is improved. The simulated rainwater use efficiency of maize was 6.1–6.5kgha−1mm−1 for traditional tillage without any fertiliser, 6.8–7.3kgha−1mm−1 for tied-ridges without any fertiliser and 11.0–12.9kgha−1mm−1 for tied-ridges with optimum fertiliser (96% soil fertility level). It is, therefore, concluded that combined use of tied-ridges and farmyard manure can enhance maize yield under wide range of rainfall conditions (annual rainfall ranging from 280mm to 680mm). The Maresha-modified ridger used in this study can be popularized among Ethiopian farmers due to its simplicity and effectiveness.

Performance evaluation of AquaCrop model for maize crop in a semi-arid environment

Crop growth simulation models of varying complexity have been developed for predicting the effects of soil, water and nutrients on grain and biomass yields and water productivity of different crops. These models are calibrated and validated for a given region using the data generated from field experiments. In this study, a water-driven crop model AquaCrop, developed by FAO was calibrated and validated for maize crop under varying irrigation and nitrogen regimes. The experiment was conducted at the research farm of the Water Technology Centre, IARI, New Delhi during kharif 2009 and 2010. Calibration was done using the data of 2009 and validation with the data of 2010. Irrigation applications comprised rainfed, i.e. no irrigation (W1) irrigation at 50% of field capacity (FC) (W2) at 75% FC (W3) and full irrigation (W4). Nitrogen application levels were no nitrogen (N1), 75kgha−1 (N2) and 150kgha−1 (N3). Model efficiency (E), coefficient of determination (R2), Root Mean Square error (RMSE) and Mean Absolute Error (MAE) were used to test the model performance. The model was calibrated for simulating maize grain and biomass yield for all treatment levels with the prediction error statistics 0.95<E<0.99, 0.29<RMSE<0.42, 0.9<R2<0.91 and 0.17<MAE<0.51tha−1. Upon validation, the E was 0.95 and 0.98; MAE was 0.11 and 1.08 and RMSE was 0.1 and 0.75 for grain and biomass yield, respectively. The prediciton error in simulation of grain yield and biomass under all irrigation and nitrogen levels ranged from a minimum of 0.47% to 5.91% and maximum of 4.36% to 11.05%, respectively. The highest and the lowest accuracy to predict yield and biomass was obtained at W4N3 and W1N1 treatments, respectively. The model prediciton error in simulating the water productivity (WP) varied from 2.35% to 27.5% for different irrigation and nitrogen levels. Over all, the FAO AquaCrop model predicted maize yield with acceptable accuracy under variable irrigation and nitrogen levels.

Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada

Wheat (Triticum aestivum L.) is the major grain crop grown in western Canada (Canadian Prairies) and soil water stress is considered the main limiting factor for crop growth. The objective of this study was to adapt and test the ability of the FAO developed AquaCrop model (v3.0) to simulate spring wheat yield and total soil water content (0–120cm layer) on the Canadian Prairies. Crop yield and soil water content data collected from five experimental sites across the Canadian Prairies from 2003 through 2006 were used in the study. Results showed that the AquaCrop model can be used to model both wheat grain yield and soil water content on the Canadian Prairies with acceptable accuracy. Overall, the relationship between observed and modelled wheat grain yield for all sites combined produced a R2 of 0.66, slope of 0.96, index of agreement (d) of 0.99, root mean square error (RMSE) of 743kgha−1 and mean absolute error (MAE) of 611kgha−1. Similarly, the comparison between observed and modelled soil water content yielded a R2 of 0.90, slope of 0.73, d of 0.99, RMSE of 49mm and MAE of 40mm. The difference between observed and modelled grain yield was only 3%, while that between observed and modelled total soil water was 2%. Consequently, AquaCrop can be a valuable tool for simulating both wheat grain yield and soil water content on the Canadian Prairies, particularly considering the fact that the model requires a relatively small number of explicit and mostly intuitive input data which can be readily available or easily collected. However, the performance of the model has to be evaluated and fine-tuned under a wider range of conditions, which we hope will be the next step.

Time trend in reference evapotranspiration: analysis of a long series of agrometeorological measurements in Southern Italy

This study aims to evaluate the potential effects of the climatic variations on the reference evapotranspiration (ET0) and, consequently, on the crop water requirements in the Apulian Tavoliere, one of the largest irrigated districts of Southern Italy. To reach this purpose, both climatic parameters (air temperature and rainfall) and estimated water requirements of ‘processing’ tomato (among the most representative irrigated crops in the district since the mid-1970s and, therefore, chosen as a study-case) were analyzed in order to find out if a time trend exists or does not. The analysis covered the period from 1957 to 2008. The analysis showed that the rainfall amounts decreased (−3.4 mm per year in the analyzed period), while air temperature increased (0.18 °C and 0.25−°C per decade for minimum and maximum, respectively). As a consequence of the climatic variation during the considered period, a growth trend of the ET0 (1.4 mm per year) and water deficit (3.2 mm per year) took place. As a consequence, the water amounts for irrigating the same crop in the considered period were growing. This increased consumption is in agreement with the perception of the farmers of the district but never documented. Through the FAO AquaCrop model, the tomato irrigation water requirements have been simulated during the considered period. The trend analysis of the seasonal evapotranspiration values simulated in 52 years confirmed the increase in tomato water requirements (0.7 mm per year).

Calibration and Testing of the FAO AquaCrop Model for Canola

Water being a major limiting factor in crop production, prediction of the growth and yield response of crop to water is important. Field experiments were conducted in 2009 and 2010 at Wagga Wagga (Australia) to calibrate and validate a water productivity model AquaCrop for canola (Brassica napus L.). The calibrated model was able to accurately simulate evolution of canopy cover, biomass accumulation, and grain yield, with low values of root mean‐square error and model efficiency, and Willmot's d statistics values close to unity. However, the model overestimated biomass and yield of canola grown in 2009 under a high moisture stress condition. Measured and simulated biomass of Skipton variety grown in 2010 to validate AquaCrop were 21.1 and 19.1 t ha−1, respectively. The grain yield was 3.18 and 3.11 t ha−1, respectively. In the drought year of 2009, measured and simulated biomass were 8.13 and 9.56 t ha−1, respectively, for the Bln3343‐Co0401 variety used for validation. The grain yield was 1.75 and 1.96 t ha−1, respectively. Although AquaCrop was able to capture the trend, it tended to slightly overestimate soil water content during the season. To standardize the conservative parameters developed in this study, further tests are recommended under different environmental and management conditions.

Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower

Farming in Serbia is traditionally rainfed. Analyses show that drought events of varying severity are frequent in this region, although there is no specific pattern. There is a distinct need for an objective assessment of the impact of drought on strategic field crops, to solve the dilemma whether irrigation is required or not. For this reason, and based on available field data, the FAO AquaCrop water driven model was selected to simulate yield and irrigation water use efficiency (IWUE) for three major field crops (maize, sunflower, and sugar beet), under two scenarios: (1) natural water supply and adequate supply of nutrients, and (2) supplementary irrigation and adequate supply of nutrients. The experiments presented here were conducted between 2000 and 2007 in northern Serbia, where chernozem soil is prevalent. Data of 2003 cropping seasons were used for local calibration, whereas the remaining years for validation. Results were such that local calibration resulted in very minor changes of AquaCrop coefficients (e.g., maize basal crop coefficient, sunflower harvest index, etc.). Simulated maize yield levels exhibited the greatest departure from measured data under irrigation conditions (−3.6 and 3.3% during an extremely dry and an extremely wet year, respectively). Simulated sunflower yield levels varied by less than 10% in 8 out of 10 comparisons. The most extreme variation was noted during the extremely wet year. The difference between simulated and measured values in the case of sugar beet was from −10.2 to 12.2%. Large differences were noted only in two or three cases, under extreme climatic conditions. Statistical indicators – root mean square error (RMSE) and index of agreement (d) – for all three crops suggested that the model can be used to highly reliably assess yield and IWUE. This conclusion was derived based on low values of RMSE and high values of d (in the case of maize and sugar beet 0.999 for both yield and IWUE, and in the case of sunflower 0.999 for yield and 0.884 for IWUE). It is noteworthy that under wet conditions, the model suggested that sunflower and sugar beet do not require irrigation, as confirmed by experimental research. These data are significant because they show that the AquaCrop model can be used in impartial decision-making and in the selection of crops to be given irrigation priority in areas where water resources are limited.