AquaCrop Model Research Articles

Irrigation and Agricultural Opportunities: Evaluating Hemp (Cannabis sativa L.) Suitability and Productivity in Lebanon

Within the prevalent challenges posed by climate change and decreasing resources, this research underscores the importance of adopting sustainable agricultural practices combined with efficient water resource management. Employing comprehensive climate and soil suitability analyses, this research analyzed the capacity of hemp (Cannabis sativa L.) to adapt to Lebanon’s heterogeneous environmental landscapes across two distinct growing seasons (autumn and spring). Both climate and edaphic suitability mapping were conducted to study hemp’s suitability. AquaCrop v.7.1 was used to simulate seed yield, biomass production, irrigation needs and yield water productivity in the different agro-homogeneous zones of Lebanon for the two considered seasons. The findings revealed that approximately 30% and 19% of Lebanon’s land exhibit suitability for hemp cultivation during the spring and autumn seasons, respectively. According to AquaCrop model simulations, under the prevailing climatic conditions, the predicted seed yield will range from 3.7 to 5.6 t ha−1 under rainfed conditions and will reach 11.1 t ha−1 for irrigated cultivation. Moreover, employing efficient irrigation techniques during the spring season showed a significant improvement in both yield and biomass of hemp. The enhancement was evident, with notable increases of 112.22% in yield and 96.43% in biomass compared to rainfed conditions. This research highlights the importance of identifying suitable regions within Lebanon capable of supporting hemp cultivation in a sustainable manner. Such research not only promises economic development but also aligns with broader global sustainability objectives.

Assessment of the AquaCrop model to simulate the impact of soil fertility management on evapotranspiration, yield, and water productivity of maize (Zea May L.) in the sub-humid agro-ecology of Nigeria

Field experiments were conducted for two seasons in Ile-Ife, Nigeria to evaluate the performance of the AquaCrop model in simulating the effects of soil fertility management on the canopy cover (CC), soil water storages (SWS), cumulative aboveground biomass (BM), evapotranspiration (ETa), grain yields, and water productivity (WP) of rainfed maize. Six levels of soil fertility management and two cultivars of maize, SUWAN 1-SR and PVA led to a 2 by 6 factorial experimental treatment and arranged in a randomized complete block design. Agronomic and environmental parameters were measured for two consecutive seasons. The AquaCrop model was calibrated using data from the wetter year. The AquaCrop model captured well the variances in the CC, R2 ≥ 0.88, RMSE ≤ 14.2, and d-index ≥ 0.97 under full and stressed soil fertility. Although the AquaCrop model over and underestimated SWS, it is still within acceptable limits. The model simulated SWS well, R2 ≥ 0.71, EF ≥ 0.97, and d-index ≥ 0.97. AquaCrop tends to underestimate ETa under rainfall and NPK variabilities. The AquaCrop model simulated grain yields excellently, R2 = 0.99, b = 1.00. The 150% of the recommended NPK application is suitable for the desired improvement in land and water productivity of the crop. The AquaCrop model predicted and captured the trends in the yields and water productivity of maize adequately under varying NPK applications. Further research is required on other cultivars of the crop and locations in the area in order to generalize the adequacy of the model.

An Evaluation of Dryland Ulluco Cultivation Yields in the Face of Climate Change Scenarios in the Central Andes of Peru by Using the AquaCrop Model

Ullucus tuberosus is an Andean region crop adapted to high-altitude environments and dryland cultivation. It is an essential resource that guarantees food security due to its carbohydrate, protein, and low-fat content. However, current change patterns in precipitation and temperatures warn of complex scenarios where climate change will affect this crop. Therefore, predicting these effects through simulation is a valuable tool for evaluating this crop’s sustainability. This study aims to evaluate ulluco’s crop yield under dryland conditions at 3914 m.a.s.l. considering climate change scenarios from 2024 to 2100 by using the AquaCrop model. Simulations were carried out using current meteorological data, crop agronomic information, and simulations for SSP1-2.6, SSP3-7.0, and SSP5-8.5 of CMIP 6. The results indicate that minimum temperature increases and seasonal precipitation exacerbation will significantly influence yields. Increases in rainfall and environmental CO2 concentrations show an opportunity window for yield increment in the early stages. However, a negative trend is observed for 2050–2100, mainly due to crop temperature stress. These findings highlight the importance of developing more resistant ulluco varieties to heat stress conditions, adapting water management practices, continuing modeling climate change effects on crops, and investing in research on smallholder agriculture to reach Sustainable Development Goals 1, 2, and 13.

Implementation of the AquaCrop Model for Forecasting the Effects of Climate Change on Water Consumption and Potato Yield Under Various Irrigation Techniques

In this study, the AquaCrop model was employed to analyze the impact of projected future climate changes on the water usage and biomass production of potato crops in Babylon, Iraq, under varying irrigation methods. The irrigation techniques evaluated included sprinkler irrigation, surface drip irrigation, and subsurface drip irrigation at depths of 10 cm and 20 cm. The study involved simulating and forecasting conditions for the year 2050, comparing them to current conditions. The model measured and predicted the evapotranspiration (ETa) and actual biomass of potato crops for 2050 using the RCP 8.5 scenarios, which outline different trajectories for greenhouse gas emissions. The AquaCrop model was calibrated and validated using statistical measures such as the R2, RMSE, CV, EF, and D, achieving a 99% accuracy level in its performance. The findings suggest that using drip irrigation systems and applying the AquaCrop model significantly mitigates the adverse effects of environmental stress on desert soils and enhances sustainable agricultural practices in arid regions.

Performance Evaluation of AquaCrop Model of Tomato under Stage Wise Deficit Drip Irrigation at Southern Ethiopia

Crop modeling is a powerful tool for predicting yield and water productivity. The aim of the study was to calibrate and validate the AquaCrop model for tomato under staged deficit drip irrigation in Ethiopia. The AquaCrop model was calibrated and validated by using the observed data of canopy cover, biomass, dry yield, and soil water content. The results showed that the model was accurate in predicting canopy cover, biomass, and dry yield under different water levels. The overall performance in simulating canopy cover of AquaCrop, biomass, and soil water content showed a good match between measured and simulated data. The calibration results indicated good performance on canopy cover with 0.96 ≤ r ≤ 1.00, 2.9% ≤ RMSE ≤ 8.0%, 7.5% ≤ CV (RMSE) ≤ 21.1%, 0.91 ≤ EF ≤ 0.99, and 0.98 ≤ d ≤ 1.00. On biomass, the model calibration values were 0.99 ≤ r ≤ 1.00, 0.3 t/ha ≤ RMSE ≤ 0.8 t/ha, 8.3% ≤ CV (RMSE) ≤ 25.2%, 0.92 ≤ EF ≤ 0.99, and 0.98 ≤ d ≤ 1.00. Soil water content displayed poor performance on calibration, with performance values of 0.54 ≤ r ≤ 0.82, 3.6 mm ≤ RMSE ≤ 24.4 mm, 1.30% ≤ CV (RMSE) ≤ 9.00%, −4.18 ≤ EF ≤ 0.00, and 0.66 ≤ d ≤ 0.81. The validation results demonstrated that the model performed well on canopy cover with 0.96 ≤ r ≤ 1.00, 3.1% ≤ RMSE ≤ 7.7%, 8.2% ≤ CV (RMSE) ≤ 20.8%, EF ≥ 0.91, and d ≥ 0.9. The model validation correctly predicted biomass with r ≥ 0.98, 0.3 t/ha ≤ RMSE ≤ 0.7 t/ha, 8.9% ≤ CV (RMSE) ≤ 21.8%, EF ≥ 0.94, and d ≥ 0.98. The model validation poorly performed in forecasting soil water content, 0.20 ≤ r ≤ 0.80, 10.2 t/ha ≤ RMSE ≤ 24.0 t/ha, 3.5% ≤ CV (RMSE) ≤ 9.0%, −22.76 ≤ EF ≤ 0.63, and 0.40 ≤ d ≤ 0.87. The AquaCrop model is easy to use, requires less input data, and has high simulation accuracy, making it a useful tool for predicting crop yields and water productivity under staged deficit irrigation in areas with limited data.

Research on Assimilation of Unmanned Aerial Vehicle Remote Sensing Data and AquaCrop Model.

Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R2 of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R2 and RMSE were 0.927 and 0.12, respectively. The canopy coverage R2 of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing.

Impacts of Climate Change and Adaptation Strategies for Rainfed Barley Production in the Almería Province, Spain

Mediterranean water-stressed areas face significant challenges from higher temperatures and increasingly severe droughts. We assess the effect of climate change on rainfed barley production in the aridity-prone province of Almería, Spain, using the FAO AquaCrop model. We focus on rainfed barley growth by the mid-century (2041–2070) and end-century (2071–2100) time periods, using three Shared Socio-economic Pathway (SSP)-based scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Using the paired t-test, Spearman and Pearson correlation coefficient, Root Mean Squared Error, and relative Root Mean Squared Error, we verified AquaCrop’s ability to capture local multi-year trends (9 or more years) using standard barley crop parameters, without local recalibration. Starting with a reference Initial Soil Water Content (ISWC), different soil water contents within barley rooting depth were modelled to account for decreases in soil water availability. We then evaluated the efficiency of different climate adaptation strategies: irrigation, mulching, and changing sowing dates. We show average yield changes of +14% to −44.8% (mid-century) and +12% to −55.1% (end-century), with ISWC being the main factor determining yields. Irrigation increases yields by 21.1%, utilizing just 3% of Almería’s superficial water resources. Mulches improve irrigated yield performances by 6.9% while reducing irrigation needs by 40%. Changing sowing dates does not consistently improve yields. We demonstrate that regardless of the scenario used, climate adaptation of field barley production in Almería should prioritize limiting soil water loss by combining irrigation with mulching. This would enable farmers in Almería’s northern communities to maintain their livelihoods, reducing the province’s reliance on horticulture while continuing to contribute to food security goals.

Calibration, testing and application of the AquaCrop model for bean crop under irrigation regimes.

Crop growth simulation models relate the soil-water-plant-atmosphere components to estimate the development and yield of plants in different scenarios, enabling the identification of efficient irrigation strategies. The aim of this study was to calibrate crop coefficients for a common bean cultivar (IAPAR 57) and assess the AquaCrop model's efficacy in simulating crop growth under different irrigation regimes (T0 - non-irrigated, T1-fully irrigated, and T2-deficit irrigated) and sowing dates (S1-March 21, S2-April 24, and S3-August 23). Successful calibration was achieved for crop seasons with suitable temperatures to crop growth (S1 and S3). However, during periods with suboptimal temperatures (April 24 season), coupled with reduced irrigation supply (T0 and T2), the AquaCrop model did not appropriately account for the combined effects of thermal and water stresses. Despite adjustments to stress coefficients, this led to an overestimation of crop growth and yield. In long-term simulations, the model successfully replicated the variability of crop water availability over cropping seasons, reflecting the impact of precipitation variations. It recommended irrigation strategies for the study region (irrigate at depletion of 120 and 170% of readily available water for sowing on March 21 and August 24, respectively) to achieve high crop yield (> 2,769kgha-1) and water productivity (1,050 to 1,445kgm-3) with minimal application depths (< 150mm). While acknowledging the need for improvements in thermal stress calculations, the AquaCrop model demonstrates promising utility in studies and applications where water availability significantly influences crop production.

Simulation of climate-adaptation responses to rainfall variability on rainfed yield anomalies

Climate adaptation offers promising approaches to cope with the detrimental impacts of climate change on rainfed agriculture, intending to mitigate yield losses and safeguard food security worldwide. This is especially critical in the semi-arid tropics, characterized by highly variable and often intensified rainfall regimes. The current study explored the potential contribution of climate adaptation measures, specifically sowing windows and supplemental irrigation (SI), to offset the impacts of rainfall variability on rainfed crop yield in the Thondamuthur block, India. Under the proposed adaptation measures, this study analyzed the relationship between historical rainfall variability (1951–2019) and rice, maize, and sorghum yield anomalies. A seamlessly integrated framework was developed to link rainfall variability analysis with yield response simulations using the AquaCrop model. Rainfall classification analysis indicated an increase in rainfall availability during the Kharif season and a decrease during the Rabi season in Period III compared with the baseline. The late-sown Kharif and early-sown Rabi crops demonstrated effective climate adaptation, with the 27th and 38th SMWs identified as optimal climate-adapted sowing weeks, respectively. Significant decreases of 10% and 12% in SI requirements were observed in early-sown Rabi maize and sorghum, respectively. The results showed that substituting Rabi rice and maize with Rabi sorghum led to reductions of 90% and 75% in seasonal SI requirements, respectively. The early-sown Rabi rice and maize contributed to 88% and 12% relative yield increases, respectively, compared with the normal-sown. This study highlights the effectiveness of climate adaptation measures in enabling farmers to withstand climate-induced anomalies.

AquaCrop Model for Evaluation of the Water Productivity of Bread Wheat has Affected Modern Irrigation System and Water Salinity in Sandy Soil

AquaCrop is a very famous model in the world for evaluating the crop water productivity. FAO has developed the AquaCrop model as a leading and powerful scientific tool for simulating the response of different field crops to managing quantitative and qualitative irrigation. Two field experiments were carried out during winter seasons 2020/2021 and 2021/2022 at the Research and Production Station, National Research Centre, El-Nubaria, El-Behira Governorate, Egypt. to evaluate yield and yield component for two bread wheat varieties (Giza 171 and Misr 1) under sprinkler and drip irrigation systems with using four concentrations of water salinity irrigation. A split-plot design with three replications was used. Wheat were randomly distributed in the main plots while, while water salinity treatments occupied the sub-plots in both seasons. Results found that the simulated and observed values of water productivity were rather well in agreement for the salinity water levels treatments SWLT. This means that differences in canopy cover CC, yield and water productivity of bread wheat crop had significant effect on the calibration results. There is an inverse relationship between the water productivity and salinity water levels treatments and bread wheat varieties, while a direct correlation was found between water productivity and irrigation systems. It could be concluded that using sprinkler irrigation systems with S1 and S2 of SWLT and Misr 1 varitey, and drip irrigation system with S1 and S2 of SWLT and Misr 1 varitiey. the maximum of water productivity due to that there is a direct relationship between the study factors, the characteristics of the canopy cover growth and yield of the bread wheat plants, the more measured values will be simulated by the Aquacrop model.

Modelling the impact of different irrigation regimes and mulching on strawberry crop growth and water use in the arsenic-contaminated Bengal basin

Replacement of water-intensive winter rice with strawberry (Fragaria × ananassa Duch.) may restrict groundwater extraction and improve water productivity and sustainability of agricultural production in the arsenic-contaminated Bengal basin. The potential of strawberry cultivation in terms of yield obtained and water use efficiency need to be evaluated under predominant soil types with mulch applications. Water-driven model AquaCrop was used to predict the canopy cover, soil water storage and above-ground biomass of strawberry in an arsenic-contaminated area in the Bengal basin. After successful calibration and validation over three seasons, AquaCrop was used over a range of management scenarios (nine drip-irrigation × three soil types × four mulch materials) to identify the best irrigation options for a drip-irrigated strawberry crop. The most appropriate irrigation of 176 mm for clay loam soil in lowland and 189 mm for sandy clay loam in medium land rice areas and the use of organic mulch from locally available jute agrotextile improved 1.4 times higher yield and 1.7 times higher water productivity than that of without mulch. Strawberry can be introduced as an alternative crop replacing rice in non-traditional upland and medium land areas of the arsenic-contaminated Bengal basin with 88% lower groundwater extraction load and better economic return to farmers.

Rendimiento de remolacha azucarera bajo riego por goteo con ecuación de Penman y modelo AquaCrop

It is necessary to estimate sugar beet yield, because studies with this crop demonstrated than in Peruvian coastal zone, could be a profitable crop. The objective of the present experiment was to know if dry matter yield of sugar beet is related with Penman’s equation, or FAO’s AquaCrop model. Experiment was made in a sandy soil, non-salty, calcareous, very poor in organic matter, with drip irrigation in Peruvian northern coast. Four treatments: two, three, four and five plant rows per irrigation drip line, in a completely random design, with four replications were utilized. Calculated fresh matter weighs with AquaCrop were between 15.5 and 24.5 Mg.ha-1, very much lesser to real ones (between 67.5 and 103.9 Mg.ha-1) hence Aqua Crop model is not effective to estimate yield of sugar beet. It is possible to estimate yield of sugar beet, with Penman’s formula, which varied between 11.40 and 27.96 Mg.ha-1 dry weight, and the real one was between 13.4 and 21.5 Mg.ha-1, with a "Root Mean Square Error" (RMSE) of 3.73.

Optimization of maize irrigation strategy in Xinjiang, China by AquaCrop based on a four-year study

Global water scarcity has become a non-negligible problem that threatens the sustainable development of agriculture. In order to alleviate the contradiction between grain demand and water resource constraints, it is particularly important to explore appropriate irrigation strategy so as to synergistically increase grain yield and water use efficiency (WUE). The AquaCrop model were locally calibrated to simulate optimal irrigation amount for different hydrological years using a four-year field measurements (from 2017 to 2020) of maize with two irrigation levels (2400 m3/ha and 4800 m3/ha) in Shihezi, Xinjiang, China. On this basis, regulated deficit irrigation (RDI) strategies were optimized based on the variation of water consumption and soil water content (SWC) during the maize growth period. Results suggest that the optimal irrigation amount under static strategy (fixed proportion irrigation in growing season) in the wet, normal, and dry years was 4733 m3/ha, 5381 m3/ha, and 6090 m3/ha, respectively. In dynamic strategies, the optimized RDI4 (65% Ir (the amount of water required for each irrigation interval) at R2-R5 stage) and RDI5 strategy (85% Ir at V6-V12 stage and 85% Ir at R2-R5 stage) can save irrigation water while maintaining high yield. Under the premise of basically maintaining high yield (18Mg/ha), compared with each year's irrigation amount of 4800 m3/ha, the RDI4 strategy can reduce the irrigation amount by 4.33% in 2017; although the irrigation amount was slightly increased by 2.77% under the RDI5 strategy in 2018, the yield could be increased by 3.65%; in 2019, the RDI5 strategy can save 49.44% of irrigation water, and the RDI4 strategy will save 24.13% of irrigation water in 2020. From this study, it is recommended that a single irrigation amount of 65% Ir in R2 to R5 stages of maize growth or 85% Ir in V6 to V12 stages and 85% Ir in R2 to R5 stages can save irrigation water while maintaining high yield (18 Mg/ha).

How global sensitive is the AquaCrop model to input parameters? A case study of silage maize yield on a regional scale

IntroductionAquaCrop is a water-driven crop growth model that simulates aboveground biomass production in croplands. This study aimed to identify the driving parameters of the AquaCrop model for the model calibration and simplification to fill the research gap in intermediate environmental conditions between sub-tropical sub-humid and temperate sub-humid climates for silage maize.MethodsTo this end, we applied global sensitivity analysis (GSA) by combining the Morris method and the Extended Fourier Amplitude Sensitivity Test (EFAST) on crop yield output. The process involved a field sampling of soil and crop of silage maize carried out in the agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019 in order to measure certain model parameters.Results and discussionIn compliance with the Morris method, 30 parameters were identified as the least sensitive, while results from the EFAST test showed 9 parameters as contributing to the highest sensitivities in the model. The results clearly point to the capacity of employing a combination of both methods to attain a more efficient model calibration. Particular root, soil, canopy development, and biomass production parameters were influential and merit attention during calibration. Instead, parameters describing crop responses to water stress were acting rather insensitive in this study condition. The insights gained from this study, i.e., assessing parameter ranges and distinguishing between less sensitive and more sensitive parameters based on environmental and crop conditions, have the potential to be applied to other crop growth models with caution.

Sensitivity Analysis of AquaCrop Model for Winter Wheat in Different Water Supply Conditions

Sensitivity Analysis of AquaCrop Model for Winter Wheat in Different Water Supply Conditions

Development of yield prediction model for wheat by using AquaCrop model with different nitrogen dose applications in Central Anatolia Region (semi arid) conditions

In this study, yield prediction was made for Tosunbey and Bayraktar bread wheat varieties under rainfall conditions and 4 different fertilizer ratios with AquaCrop model, one of the plant growth models. In this experiment conducted at Haymana Ikizce Research Farm, actual field observations and model predicted grain yield, biomass, and green area coverage ratio were evaluated. Mean deviation (α), standard error (RMSE), and model efficiency coefficient (E) tests were used to determine the performance of the model. The AquaCrop model was calibrated in the first year and validated based on observational data collected in the first and second years of the experiment, respectively. Based on the results obtained, it was observed that the AquaCrop model simulated grain yield at different levels of nitrogen fertilizer applications with higher precision for Bayraktar variety. For Bayraktar variety, grain yield E = 0.93 in the first year and 0.99 in the second year for grain yield, and E = 0.83 in the first year and 0.98 in the second year for biomass, indicating excellent agreement between model and observation was found. In Tosunbey variety, first-year grain yield E=0.66 and 2nd year grain yield 0.76 were found. The 2nd year RMSE value for grain yield of Bayraktar variety was 0.266, and the 2nd year RMSE value for the grain yield of Tosunbey variety was 0.664 and found to be statistically compatible. Grain yield, biomass, and percent cover (CC) values obtained from the model were found to be highly consistent with field observations.

Climate Change and its Impact on Water Requirement and Productivity of Bengal Gram Over Different Agro-climatic Zones of Tamil Nadu

Background: The AquaCrop model for bengal gram successfully matches the biomass and yield of reported crop data under various planting dates, according to results that have been validated. Further this study was carried out in different agroclimatic zones of Tamil Nadu to assess variations in yield and water requirement. Methods: In order to simulate realistic yields for the Bengal gram in response to planting date impacts in the Guziliamparai Block in the Dindugal District of Tamil Nadu, the study parameterized and verified performance of the AquaCrop model. Four field trials planted in D1 - 1st November, D2 - 15th November, D3 - 1st December, and D4 - 15th December provided the model calibration data. Result: Fruit yield and biomass had R2 values of 0.9 and 0.8, respectively, indicating strong agreement between observed and model-simulated data. Fruit output and biomass were found to have RMSE values of 0.4 and 0.2, respectively. The NRMSE values for fruit yield and biomass were determined to be 0.3 and 0.1, respectively. The AquaCrop model somewhat overestimated fruit production and biomass since the BIAS was less than 0.4 and 0.2 for yield and biomass, respectively. Fruit production and biomass showed less agreement between simulated and actual D levels (0.4 to 0.6). Across Tamil Nadu's ACZ, a 20C temperature increase decreased the yield by an average of 6%, with minimums of 2% and maximums of 13%. An rise in temperature of 3°C and 4°C, respectively, decreased the yield by 9% and 19% on average.

Enhancing in-season yield forecast accuracy for film-mulched wheat: A hybrid approach coupling crop model and UAV remote-sensing data by ensemble learning technique

Accurate in-season yield forecasts for field-scale crops are crucial for both farmers and decision-makers. Common methods for yield prediction are limited by the availability of unknown weather data (process-based crop models) and the failure to consider yield formation processes (statistical models based on unmanned aerial vehicle (UAV) images), respectively. Furthermore, previous studies focused only on crops without mulching, yet mulching is an important agronomic approach to increase grain yield in the arid areas of northwest China. We aim to develop a hybrid approach coupling crop model and UAV data through ensemble learning to achieve in-season yield forecasts for film-mulched wheat. A four-year field experiment was constructed (2018–2020 and 2021–2023). We first calibrated AquaCrop using data from 2018 to 2020, and historical weather data were employed to drive AquaCrop for predicting yields in 2021–2023. Next, statistical models were constructed to predict yields based on spectral and textural indices calculated from UAV images. Finally, a hybrid approach coupling the AquaCrop model and remote-sensing data was developed using ensemble learning technique. Quantifying the relative contribution of features used SHapley Additive exPlanations values. The results indicated that AquaCrop yield forecasts exhibited considerable uncertainties (R2: 0.53–0.63; NRMSE: 16.54%–14.83%). The interpretation of yield for remote-sensing data was influenced by background and saturation effects, reaching its highest accuracy at the heading stage (R2 was 0.80, NRMSE was 11.88%). Ensemble learning demonstrated strong performance compared to machine learning algorithms. The coupling model combined the advantages of crop and statistical models by the ensemble learning algorithm, achieving accurate yield predictions more than 40 days before harvest (heading stage) based on AdaBoost regression (R2 was 0.88, NRMSE was 8.40%). The most important forecasting factors affecting yield prediction were the textural indices, followed by the AquaCrop simulated values. Overall, the coupled model showed good performance in predicting the in-season yield of film-mulched wheat, which provided new insights into farm-scale yield prediction. Further validation of the generalizability of the coupled model in different scenarios is required in the future to improve the applicability of the model in actual production practice.

Cotton yield responses to climate change and adaptability of sowing date simulated by AquaCrop model

The feedback mechanism of cotton yield to climate change is important knowledge for cotton planting planning and stable production. However, the impact of sowing date on cotton yield in China's main cotton producing areas under future climate change is still unclear. To deal with it, this study first evaluated the applicability of the AquaCrop model using actual observed cotton growth and yield data. Then, based on the 27 Global Climate Models (GCMs) multi-model ensemble data in the Sixth Coupled Model Intercomparison Project (CMIP6), the future climate changes in China 's main cotton producing areas from 2021 and 2060 (2040 s) and 2061–2100 (2080 s) under two typical Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP5–8.5 were predicted. Finally, the effects of different sowing dates on cotton yield under SSP2–4.5 and SSP5–8.5 scenarios were simulated. The results showed that: (1) AquaCrop model had good applicability and it could accurately simulate cotton growth and yield. (2) The temperature under the SSP2–4.5 and SSP5–8.5 scenarios increased significantly compared with the baseline period (1985–2020). Precipitation during the cotton growth period mainly showed an increasing trend, but the spatial and temporal variability was large. The reference crop evapotranspiration (ETo) during the cotton growth period mainly showed a decreasing trend under the SSP2–4.5 (2040 s) scenario, while it mainly showed an increasing trend in other future periods. (3) The cotton yield under the SSP2–4.5 and SSP5–8.5 scenarios increased by an average of 10.51% and 13.67%, 14.76% and 28.04% in 2040 s and 2080 s compared to the baseline period (1981–2015), respectively. (4) Early sowing was beneficial to increase cotton yield, cotton yield sown on March 31 increased by an average of 5.32% compared to May 10. In conclusion, future climate change will have positive impacts on cotton production in China, and early sowing is an effective adaptation measure to cope with climate change.

Evaluation of the AquaCrop Model for Simulating Cotton Yield Under a Semi-Arid Environment and Different Field Management Practices

Evaluation of the AquaCrop Model for Simulating Cotton Yield Under a Semi-Arid Environment and Different Field Management Practices