FAO AquaCrop Model Research Articles

Climate change impacts on rice yield of a large-scale irrigation scheme in Malaysia

The yield of lowland rice is prone to climatic factors and farming practices, among which water management and weed control are crucial. Crop simulation models can help evaluating the impacts of these factors and practices on yield growth. This study assessed the impacts of climate, irrigation practices, and weed control measures on the yield of lowland rice by using FAO-AquaCrop model. Necessary data were generated by well-planned field experiments during two consecutive rice-growing seasons (off-/dry season: January–April and main-/wet season: July–October) in 2017. Ensemble climate data was downscaled from eighteen General Circulation Models (GCMs) under three Representative Concentration Pathway (RCP) scenarios (RCP4.5, RCP6.0 and RCP8.5) both for baseline (1976–2005) and future (2010–2099) periods. AquaCrop model was calibrated and validated with observed data with regard to Normalized Root-Mean-Square Error (NRMSE), Prediction Error (Pe), Mean Absolute Error (MAE), and Index of Agreement (d). The model simulated biomass, yield and productivity of rice satisfactorily. It predicted 13.6%, 14.1% and 15.3% increase in yield under RCP4.5, RCP6.0 and RCP8.5 emission scenario, respectively for the off-season in the future compared to the baseline period. The corresponding increase in yield is 13.9%, 14.5% and 15.2% for the main-season. The model simulated reduced yield under water stress and poor weed control, with yield reduction mounting to 65% under worse weed control. However, proper weed control and water management practices will likely augment yield under changing climate. The stress exerted by rising temperature on yield will likely be compensated by the ensuing increase in CO2 fertilization. The predicted susceptibility of rice yield to weed infestation and water stress under changing climate will help devising proper management practices for sustainable rice productivity in the future.

Calibration and Evaluation of the FAO AquaCrop Model for Canola (Brassica napus) under Varied Moistube Irrigation Regimes

The AquaCrop model was calibrated and validated for canola (Brassica napus) under Moistube irrigation (MTI) and various water regimes [(i) 100%, (ii) 75%, and (iii) 55% of crop water requirement (ETc)] over two seasons, 2019 and 2020. The normalised root mean square (nRMSE), Model Efficiency (EF), R2, and the Willmot’s index of agreement (d) statistics were used to evaluate the model’s efficiency in simulating biomass (B), canopy cover (CC), yield (Y), and harvest index (HI). The calibration results indicated the model simulated with accuracy the CC (under 100% ETcR2 = 0.99, EF = 0.92, nRMSE = 6.4%, d = 0.98) and 75% ETc (R2 = 0.99, EF = 0.92, nRMSE = 10.3%, d = 0.98). The model simulated CC well for validation for 100% ETc (R2 = 0.97, EF = 0.93, nRMSE = 22.5%, d = 0.98) and 75% ETc (R2 = 0.84, EF = 0.45, nRMSE = 59.2%, d = 0.86) irrigation regimes. Final biomass simulations were reasonably good under 100% ETc, 75% ETc, and 55% ETc irrigation regimes (R2 > 0.90, d > 0.65). The study showed the usefulness of AquaCrop for assessing yield response of canola to full and deficit irrigation scenarios under MTI.

Impact of Climate Change on Some Agricultural Crops Distribution and Productivity in Georgia

Abstract Under the increase of the concern for food security in the world, mainly caused by water resources shortages, the forecast and determination of crop yield at regional scale has been considered as a strategic topic. This study has been conducted to assess the possible impacts of the climate change on cereal crops productivity and irrigation requirement for two main producing regions of Georgia, according to the current crop pattern, and for the 2050s periods. With this aim, water-driven FAO-AquaCrop model has been used. Furthermore, ongoing and forecasted changes, up to the end of the century, in agro-climatic zones relevant for cereals production have been assessed. The climate change data was generated for RCP4.5 scenario through the global circulation model ECHAM4.1, dynamically downscaled on the region via regional climate model (RegCM4.1). Results show overall increase in cereal crop yields, but also enhancement in water shortages even considering optimum management practices under rainfed conditions. Based on the results obtained, recommendations have been developed for adaptation measures to the climate change for the Georgia Agriculture sector.

КОЛИЧЕСТВЕННАЯ ОЦЕНКА ВЛИЯНИЯ ИЗМЕНЕНИЙ КЛИМАТА НА ЭВАПОТРАНСПИРАЦИЮ И УРОЖАЙНОСТЬ СЕЛЬСКОХОЗЯЙСТВЕННЫХ КУЛЬТУР В ЦЕНТРАЛЬНОМ РАЙОНЕ ИНДИИ

The present work focuses on estimation of future evapotranspiration of paddy, maize, soybean and assessment of yields of these crops under RCP scenarios 2.6, 4.5, and 8.5 for years 1997-2099 using FAO Cropwat and AquaCrop yield simulating models for the Sehore district, in central state of India. Statistically downscaled General Circulation Model CanESM2 data were used as input to Cropwat and AquaCrop tools for generation of future crop evapotranspiration and crop yield data. The AquaCrop yield model was first checked for its suitability and accuracy in prediction of yield for years 1997-2010. The future scenario RCP 8.5 shows the highest reduction in the yield of paddy (-8.5%), maize (-4.52%), and soybean (-3.93%) during the future period. It was concluded that the FAO AquaCrop model can be applied to many other crops as well as in the other regions to formulate proper cropping strategies that will help to decrease the risks due to future climate change.

Using Drip Irrigation to Mitigate the Effects of Drought in Food Production: A Case Study of Production of Beans during Dry Spells in Uasin Gishu County, Kenya

Over 80 percent of Kenya’s freshwater withdrawals are utilized in agricultural production. The increase in human population means that even more water will be channeled towards food production. Improving water productivity is the most appropriate strategy for increasing food production for a fast growing population due to its consideration of the sustainability of water resources. Kenya predominantly depends on rain-fed agriculture for its food production, and this exposes the country to acute food shortages during droughts. Beans (Phaseolus vulgaris L.) are the primary source of protein for most households in Kenya. Despite this fact, there is a supply deficit during dry spells. An irrigation project was carried out at Moi University with an objective to study the effect of deficit irrigation as a mitigation measure to curb the shortage of beans during dry spells while ensuring sustainable use of water resources. This was carried out through modelling of water productivity (WP) and yield (Y) of beans using the FAO AquaCrop model. Field experiments were set up in a Randomized Complete Block Design (RCBD) arranged in split plots and replicated three times. Two water treatment strategies were employed (deficit irrigation, full irrigation). In the full irrigation supply, the crop was kept at 100% of irrigation requirement (T100) and data collected from these plots was used in Aqua Crop model calibration. There were three levels of deficit irrigation used: 80%, 60%, and 50% of irrigation requirement (T80, T60, T50), these were used in model validation. The highest WP, as well as the lowest yield reduction of 2.4%, was observed in the T80 treatment, this signifies water savings of up to 20%, which translates to 750 m3/ha. The highest yield reduction of 59.8% was obtained in T50 treatment, coupled with a drop in WP. Deficit irrigation results in yield reduction as observed in this study, but the amount of water saved can be used to irrigate more land or be utilized elsewhere. Consequently, it is necessary for Kenya to adopt deficit irrigation to ensure food security during dry spells while at the same time ensure sustainable water use.

Assessing the Effect of Irrigation Water Management Strategies on Napier Productivity—A Review

Napier, a fast growing and perennial grass has a dry matter (DM) yield potential of 78 tons/ha/yr. However, under water availability constraints Napier’s yield potential reduces to 62 tons/ha/yr. In an effort to attain Napier’s yield potential, irrigation management strategies have been integrated into its production to provide the highest productivity. This review assesses the effect of irrigation water management strategies on Napier productivity and also looks at future perspectives. Application of these strategies i.e., precision irrigation, deficit irrigation, and application of biophysical models, can increase Napier’s yield potential to 112 tons/ha/yr. Review findings revealed that there is a need to close the knowledge gap on response of Napier productivity to different irrigation water management strategies. The future perspective explores the potential of the FAO AquaCrop model in provision of pre-season decision-making on irrigation strategies due to its relatively low cost and simplifications required in parameterization.

Simulating Maize Yield Response to Depletion of Available Soil Water and Nitrogen Management under Drip Irrigation with the FAO AquaCrop Model

Crop simulation models of different complexity have been developed for predicting the effects of soil, water and nutrients on growth and water productivity of different crops. These models are calibrated and validated for a given region using the data generated from field experiments. Therefore, AquaCrop model was calibrated and validated for grain maize (Single Cross 260) under varying irrigation and nitrogen levels. The experiment was conducted at the research farm of the agricultural college, Islamic Azad University, Shiraz during summer season 2011 and 2012. Irrigation treatments consisted of different levels of depletion of available soil water. The four levels of moisture depletions considered in the study were 20, 40, 60 and 80 percent. Nitrogen application levels were 150 (N1), 200 (N2), 250 (N3) and 300 kg ha–1. Root Mean Square error (RMSE), Prediction error (Pe), coefficient of determination (R2) and normalized root mean square error (RMSEn) 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 4 < Pe < 5 per cent, 0.64 < R2 < 0.81 and 469 < RMSE < 786 t ha–1. Upon validation, Pe between 10 and 6; R2 between 0.65 and 0.76 and RMSE between 1062 and 1293 for grain and biomass yield, respectively. The highest and the lowest accuracy to predict yield and biomass under all nitrogen levels was obtained at I1 (MAD: 20%) and I4 (MAD: 80%) treatments, respectively. The results of the present study show that the AquaCrop model simulates aboveground biomass more accurately than grain yield. Also, model cannot provide satisfactory results under severe water stress conditions.

Application of the AquaCrop model in decision support for optimization of nitrogen fertilizer and water productivity of soybeans

Modelling of the effects of water and different levels of nitrogen on evapotranspiration and water productivity of rainfed soybeans is very important in optimising resource use in the production of the crop. The objective of the study was to model evapotranspiration, soil water storage and water productivity of rainfed soybeans under different levels of nitrogen fertilizer by using the FAO AquaCrop model. Field experiments were conducted at the Teaching and Research Farms of Obafemi Awolowo University, Nigeria in the rainy seasons of 2015 and 2016. There were five levels of nitrogen, which are 00, 25, 50, 75 and 100% of the recommended nitrogen applications and two varieties of soybeans, which produced a 2 by 5 factorial experimental design. The data of the wetter year 2015, were used for calibration of the AquaCrop model because AquaCrop is water driven. The 2016 data were used for the validation. The AquaCrop model simulated canopy cover with R2 and EF > 0.90, d-index ≥ 0.99. The model captured the trend of the soil water storage well, R2 and EF ≥ 0.70. The AquaCrop model simulated soil water storage below wilting point for seasonal rainfall less than 600 mm, and it did not overestimate it. The model predicted evapotranspiration with R2 and EF ≥ 0.89, d-index = 1.00. Above ground biomass was overestimated even though R2 ≥ 0.98. Although, nitrogen stress reduced seed yield and water productivity, there was no under or over estimation of the seed yields. They were predicted with low error under the different levels of nitrogen fertilizer, R2 ≥ 0.99, EF and d-index ≥ 0.99. The AquaCrop model is suitable for simulating canopy cover, soil water storage, evapotranspiration, and seed yield of rainfed soybeans with different levels of nitrogen fertilizer under temporal distribution of seasonal rainfall. Therefore, it can serve as a useful tool for smallholder farmers in predicting productivity of soybeans and optimising resource allocation, land and water use in the tropical farming systems.We recommend simulation of the effects of pest on biomass, seed yield and water productivity by subsequent versions of the AquaCrop model. In addition, incorporating an economic sub-unit in the model will enable users to make financial decisions.

Crop yield simulations in Mexican agriculture for climate change adaptation

Climate change is considered a serious threat to food security worldwide. In this study, yields of maize, beans, wheat, soybean, sorghum, barley and potato were modeled with 28 future climate change scenarios. Our results reduce the information gap that is frequently reported for Mexico and will contribute to better knowledge on spatial impact of climate change. We applied FAO AquaCrop model for 22 case studies located in 14 states of Mexico. Climate change scenarios were: CNRM, GFDL, HADGEM, MPI and Ensemble REA, with two radiative forcing concentrations (4.5 and 8.5 W m –2 ) and three time horizons (2015-2039, 2045-2069, and 2075-2099). The results show decreases in yields of most of the case studies as a consequence of a decrease in the amount and distribution of precipitation. Maize yield in warm dry climates could decrease up to 84% in the most severe scenarios. Beans could decrease from 10 to 40% in the north of the country, while in the northwest a 15% decrease in wheat yield is predicted. Soybeans could benefit, with increases from 15 to 40%. Sorghum and potatoes are expected to decrease for all the case studies, while barley would have increases and decreases. The results suggest differentiated impacts according to crops and regions studied. We concluded that agriculture requires better focused strategies and policies (attention on crop and spatial distribution).

Modeling and Evaluation of AquaCrop for Maize (zea mays L.) under Full and Deficit Irrigation in Semi-Arid Tropics

FAO AquaCrop is a simulation model that predicts the effects of soil, climate, water and crop growth on water productivity, yield and its attributes of various crops. In the present study, performance evaluation of AquaCrop model for maize was assessed for rabi maize during 2015 at Water Technology Centre, College of Agriculture, Rajendranagar, Hyderabad. The experiment was laid in a randomized block design with eight treatments in three replications. The treatments comprised of surface and drip irrigation schedules based on Epan viz., surface irrigation at 0.6 IW/CPE ratio (T1), 0.8 IW/CPE ratio (T2), 1.0 IW/CPE ratio (T3), 1.2 IW/CPE ratio (T4), drip irrigation at 0.6 Epan (T5), 0.8 Epan (T6), 1.0 Epan (T7) and 1.2 Epan (T8). The model was evaluated using crop data resulted from the experiment under varying water application methods and levels. Simulation performance was assessed with statistical parameters viz., statistical co-efficient of determination (R2), prediction error (Pe), model efficiency (E), root mean square error (RMSE) and mean absolute error (MAE). The model results are in quite agreement with practical values for grain yield, biomass and water productivity with model efficiency of 0.99, 0.92 and 0.71, coefficient of determination (R2) of 0.90, 0.91 and 0.93 with an RMSE of 0.24, 0.10 and 0.05, respectively. The model prediction errors in simulation of grain yield, biomass and water productivity under all treatments ranged from 1.4% to 11.9%, 1.4% to 16.1% and 4.85% to 25.9%, respectively. The highest and lowest prediction accuracy for grain yield, biomass and water productivity were in drip irrigation at 1.2 Epan and surface irrigation at 0.6 IW/CPE ratios. It is inferred that FAO AquaCrop model is suitable for predicting grain yield, biomass, water productivity and green canopy cover with acceptable range of under and over predictions for maize in semi-arid tropical climate.

Novel approach of upscaling the FAO AquaCrop model into regional scale by using distributed crop parameters derived from remote sensing data

Distributed crop models were considered a useful tool for allocating rational water resources. However, how to upscale the crop model into the regional scale is a key issue because the associated crop parameters tend to have spatial heterogeneity according to their feedback to the environment. The remote sensing may provide a possible method for generating the spatial distribution of crop parameters, thereby improving the upscaling approach. On this basis, this study aims to achieve the spatial distribution of crop parameters and establish the distributed AquaCrop-RS model by means of remote sensing. The results showed that the distribution of two crop parameters, namely, the maximum canopy coverage CCx* and the relative biomass Brel* was significantly heterogeneous with the spatial variability coefficient of 8% and 8.5% for CCx* in 2012 and 2013, respectively, and that of 6.3% and 4.6% for Brel*. Compared with the AquaCrop-GIS model without introducing the spatial parameters, the simulation accuracy of AquaCrop-RS model was improved by 20% in regional crop water consumption and by 26–36% in regional yield. The new upscaling approach could be an effective and promising method of crop model upscaling.

Calibration and validation of FAO-AquaCrop model to estimate the total biomass and yacon root yield

Due to the current water scarcity in the world, it is extremely important to improve the use of this natural and exhaustible resource in agriculture, by contributing to increase agricultural production and sustainability. Several models of crop growth simulation were developed to predict the edaphoclimatic effects on crop yield. These models are calibrated and validated for a given region using the data generated from field experiments. Therefore, the objective of this study was to calibrate and validate the FAO AquaCrop model for yacon (Smallanthus sonchifolius) crop in a tropical climate. The experiment was conducted in an experimental area located in the municipality of Ibatiba, state of Espirito Santo (Brazil) during the years of 2013 and 2014. The calibration was done using the Autumn planting and validation with the Winter and Spring plantings. For the statistical analysis, the coefficient of determination, Willmott concordance index, bias for the systematic error, root mean square error and the mean absolute error to test the model performance were used. In general, the FAO AquaCrop model predicted the root yield, total biomass and harvest index with acceptable accuracy, and with deviations of less than 6% for total and root biomass. Late planting of yacon showed a reduction in yield as well as total biomass. Keywords: Smallanthus sonchifolius, root yield simulation, modelling, agrometeorology DOI: 10.25165/j.ijabe.20201303.5012 Citation: de Sales R A, Xavier A C, de Oliveira E C, de Oliveira F L, da Silva D M N, Berilli S S. Calibration and validation of FAO-AquaCrop model to estimate the total biomass and yacon root yield. Int J Agric & Biol Eng, 2020; 13(3): 123–128.

Sorghum best practice management recommendations based on AquaCrop modeling scenario analysis in various agro-ecologies of KwaZulu Natal, South Africa

Nationally recommended sorghum planting dates in South Africa are inaccurate since they are similar for all sorghum growing regions, irrespective of climatic and soil variations within agro-ecological regions The objective was to apply a well–calibrated and tested AquaCrop model to formulate optimal planting dates and best practice management recommendations for rainfed sorghum production in three different agro–ecologies of KwaZulu–Natal, South Africa (Ukulinga, Deepdale, and Richards Bay). The FAO AquaCrop model was used to generate 10 planting dates for the different agro–ecologies. Crop files for two sorghum genotypes Ujiba (landrace) and PAN8816 (hybrid) were used for model simulations, where 10 year historical climate data was considered. Seasonal rainfall, amount of soil evaporation, crop transpired water, and ground canopy cover were identified as key parameters influencing sorghum biomass, yield and water productivity. The following optimal dates were identified at Ukulinga (7 September - 24 November), Richards Bay (23 December - 3 January), and Deepdale (21 December - 3 January). These were recommended to update generic national recommendations for sorghum for agro-ecologies represented by the respective sites. High soil evaporation relative to transpiration in all agro-ecologies indicated that the low hanging fruit in improving sorghum yields was strategies use of strategies that reduce soil evaporation and increase soil water retention. Further research can look at calibrating and validating AquaCrop for best practice management recommendations suggested in this study to predict with accuracy the impact of such strategies on yield and water productivity of sorghum in various agro-ecologies.

Calibration and validation of FAO-AquaCrop model to estimate the total biomass and yacon root yield

Calibration and validation of FAO-AquaCrop model to estimate the total biomass and yacon root yield

Optimizing Irrigation Water and Nitrogen Fertilizer Levels for Tomato Production

Background:Though nitrogen and water are key factors for tomato production, their optimum integration is not well identified in the study area. Therefore, optimizing irrigation level and nitrogen fertilizer rates are crucial to boost tomato yield as well as for better nutrient and Water Use Efficiency (WUE).Objective:The objective of the present study was to determine the optimum irrigation water and nitrogen fertilizer levels for higher tomato yield, improved water and nutrient use efficiencies.Methods:Split plot design was implemented with three irrigation levels expressed as a percentage of potential Evapotranspiration (ETc) allotted to the main plots and four nitrogen levels as sub-plots. Climate data were imported to AquaCrop model climate dataset for determining irrigation water amount and irrigation scheduling. Irrigation scheduling was determined using the FAO AquaCrop model and the crop evapotranspiration (ETc) in AquaCrop model was determined using Penman-Monteith method.Results:Irrigation water and nitrogen fertilizer levels markedly influenced the growth and yield performance of tomato, nutrient residue, Agronomic Efficiency (AE), Partial Factor Productivity (PFP) as well as Water Use Efficiency (WUE). With this, the most influential factor for tomato production was the nitrogen level rather than irrigation.Conclusion:In this study, higher growth and yield performance as well as, better water and nutrient use efficiencies of tomato were obtained while the irrigation level of 75% ETc is interacted with a nitrogen fertilizer rate of 92 kg N/ha.

Optimizing Irrigation Water and N Levels for Higher Yield and Reduced Blossom End Rot Incidence on Tomato

This study was conducted during 2018/19 under drip irrigation in the dry season to examine the effect of irrigation and N levels on yield, economic performance, and incidence of blossom end rot (BER) on tomato. A 3 × 4 factorial design with subdivided plots was implemented. Three irrigation levels (50%, 75%, and 100% ETc) were randomly assigned in the main plots and four N levels (0, 46, 92, and 138 kg ha−1) to the subplots. Climate data were imported into AquaCrop model climate dataset for determining irrigation water amount and irrigation scheduling. Irrigation scheduling was determined using the FAO AquaCrop model. Data were subjected to analysis of variance (ANOVA) using GenStat software. There was significant interaction effect of irrigation and N levels on yield, yield parameters, and BER incidence on tomato. Highest fruit diameter and fruit length were attained from the combined application of 75% ETc and 138 kg N ha−1. Besides, maximum fruits per plant and marketable yield were obtained under combined use of 100% ETc with 138 kg N ha−1and 75% ETc with 92 kg N ha−1, respectively, whereas lowest yield performance was recorded when 50% ETc is coupled with 0 kg N ha−1. However, highest (21.91%) and lowest (7.03%) BER incidence was found under the combined use of 50% ETc and 0 kg N ha−1100% ETc and 92 kg N ha−1, respectively. The economic analysis revealed that application of 46 kg N ha−1was economically feasible irrespective of the irrigation water levels.

Calibration and validation of the FAO-AquaCrop Model for cassava in the Dong Xuan cultivation area of Phu Yen province using irrigation rainfall

Calibration and validation of any model before using for a specific study is essential. In this regard, the aim of this study was the calibration and validation of the FAO-AquaCrop model to predict the cassava yield in the Dong Xuan district belonging to Phu Yen Province of Vietnam. Based on the appraised results through calibration and validation processes whereby simulated biomass and crop yield under irrigation water came from rainfall during period 2000–18 could be concluded that the FAO-AquaCrop model, with the index of agreement (d) and correlation coefficient (R2) up to 0.85 and RMSE lower than 0.30 for simulating the biomass and cassava yields had confirmed the appropriate accuracy of the model performance. The results confirmed that the FAO-AquaCrop model was suitable for simulating the biomass and cassava yields in the Dong Xuan cassava cultivation area.

Assessment of AquaCrop model in simulating maize canopy cover, soil-water, evapotranspiration, yield, and water productivity for different planting dates and densities under irrigated and rainfed conditions

A well-tested crop model can be an important tool in assessment of different crop management scenarios to improve crop yield and water productivity. In this study, the FAO AquaCrop model was evaluated for predicting maize (Zea mays L.) canopy cover (CC), available soil-water (ASW), grain yield, crop evapotranspiration (ETc), and water use efficiency (WUE) when grown at three different planting dates and densities under rainfed and subsurface drip-irrigated conditions in Nebraska, USA. The model adequately simulated CC in 2011 with root mean squared error (RMSE) and model efficiency (EF) in the range of 5.3 to 12.7% and 0.42 to 0.94, respectively; while in drier 2012, CC was substantially under-predicted with higher RMSE of 24.4%. ASW was consistently underestimated in both years with negative EF value for most treatments in 2011, while in 2012, this trend was more pronounced in irrigated treatments, especially during mid-season. ETc estimates were marginal with prediction error of up to 15% in 2011 and 18% in 2012, and overall RMSE of 35 mm for both years. Simulated grain yield was acceptable in 2011 with deviations from measured data up to 8% while in 2012, the deviations were much higher up to 40% for rainfed treatments with overestimations in both years. The errors in ETc and yield simulations are considered high, especially when considering the model application in water-limiting or/and rainfed conditions, and low-yielding areas in which these errors can be a substantial portion of the total yields produced and in seasonal total ETc from production fields and can cause challenges for growers and decision-makers. The overestimation of yield and underestimation of ETc resulted in overestimation of WUE, producing inconsistent estimates in both years. Detailed analyses of model performance and potential reasons for the discrepancies and areas that require improvements in the model are presented.

Performance of the Aquacrop model for bean (Phaseolus vulgaris L.) under irrigation condition

With the evident climatic threats and the limitation of agronomic information for the bean crop, the use of agricultural models is necessary to broaden and disseminate technical knowledge of crop forecasting. The aim of this work was to evaluate the FAO AquaCrop model for bean crop under different levels of irrigation water in agrometeorological conditions of Northeastern Brazil at megatheramal and humid climate. The research was conducted in the period from 11/17/2015 to 02/01/2016. The experimental design was randomized block with four replicates. Treatments were composed of six levels of irrigation on the basis of crop evapotranspiration (ETc) fractions (25, 50, 75, 100, 125 and 150% of ETc). The irrigation effect was evaluated from biomass, dry matter and grain yield data that were observed and simulated using Aquacrop model. One linear meter of plants was collected every 10 days for biometric and destructive analyses. In addition, the soil water content simulated from model was compared with measurements performed by time domain reflectometry. The AquaCrop model was calibrated for common bean during the dry season (October to March) for the region in the 2015/2016 harvest season using experimental data for 100% of ETc. The accuracy of the calibration and validation model was evaluated based on the root mean square error (RMSE), Willmott’s index of agreement (d), correlation coefficient (r) and percentage deviation (D). The model showed good performance between observed and simulated values for soil water content, dry biomass accumulation and grain yield in several water conditions and can assist decision making and water management in irrigated crops.

Use of AquaCrop model for estimating crop evapotranspiration and biomass production in hilly topography

The use of crop models in sloping areas is questionable when relief is not taken into account, as relief affects infiltration, radiation, and aerodynamic processes. The objective of this work is to evaluate the performance of FAO-AquaCrop model in simulating crop evapotranspiration, water balance, and biomass production in hilly areas using in situ measurements. The experiment was conducted in the Cap-Bon region, north-eastern Tunisia, on two wheat fields located on opposite sloping rims (A and B) and one control field (C) on a flat terrain: field A is SE-oriented with 5% slope and B is NW-oriented with 6% slope. Three flux stations were used to monitor automatically actual evapotranspiration (ET) and climatic factors whereas soil moisture and biomass production were measured manually. Model’s outputs were compared to actual measurements using statistical indicators: slope of the regression line, root mean squared error (RMSE), and the coefficient of determination (R2). Actual ET varied between 1 and 2 mm during crop initial stage and 3–4 mm during mid-season stage. The ET/ETo ratio during mid-season was 0.81, 0.74, and 1.03, respectively for fields A, B, and C, well below the commonly used value of 1.15. Comparison between measured and simulated ET shows a substantial overestimation of the model in sloping fields with 6–20% higher averages and a RMSE of 0.47–0.77 mm/day. AquaCrop seems to simulate reasonably well water balance, particularly in flat conditions. RMSE of water content in the top 100 cm soil-layer was in the range 41–67 mm/m, representing a relative error of 11–21%. Simulated and measured biomass values presented similar trends (R2 = 0.86–0.94) with a systematic difference, indicating that AquaCrop outputs could be improved by a correction factor.