AquaCrop Model Research Articles

Modelling the response of tomato on deficit irrigation under greenhouse conditions

To optimize crops irrigation strategy is crucial to improve the production sustainability in a climate change scenario characterized by an ever-increasing water shortage. Crops simulation models, combined with experimental data, can be useful tools. AquaCrop, a crop water productivity model, has been widely used to reach this aim in open field condition but it has been limited adopted under greenhouse conditions. This study aims to calibrate and validate AquaCrop model through a greenhouse tomato cultivation using a split plot experimental design. Crop irrigation management was the main treatment [full irrigation (FI) at 100 % crop evapotranspiration (ETc) vs. deficit irrigation (DI) at 75 % of FI] and fertilization [no fertilization, mineral fertilization, organic fertilization with compost, and organic fertilization with sieved (< 2 mm) compost] the subplots. Fresh yield, above-ground biomass, water productivity, and net irrigation requirements were simulated. The validated model also permitted to evaluate the impacts of changing temperature outside the greenhouse on fruits yield, biomass, and water productivity using 30 years of historical weather data. The results showed that the model accurately estimated crop parameters, although it tended to overestimate soil water content. On average, DI reduced fruit yield by 14.1 % compared to FI. Over the last 30 years, the validated model permitted to calculate an average fruits yield reduction due to DI of 12.6 %. Our findings suggest that models like AquaCrop can assist in optimizing greenhouse agriculture by predicting crop performance under different conditions. Our study also highlights that external temperature and AquaCrop can be used to estimate tomato yields in the greenhouse by providing decision support tools for end-users (farmers, farmer associations, and policymakers) seeking sustainable and efficient greenhouse farming practices in a changing climate.

Influence of Climatic Factors on the Water Footprint of Dairy Cattle Production in Hungary—A Case Study

Our study aims to provide a look at how the production of dairy cattle is affecting water resources in Hungary. Utilizing the AquaCrop model and field data from a selected field in Hungary, we focused on the evapotranspiration (ET) and water footprint (WF) of maize (the dominant component of silage mixes), while for other feed crops, we obtained data from scientific literature sources. We also considered drinking and servicing water consumption of dairy cattle, utilizing observations from a specific farm, as well as estimating potential heat stress at the country level. Our findings indicated increasing trends of crop ET as well as biomass production for maize, without significant correlations between the two parameters. Spatiotemporal analysis revealed a significant rise in the number of days with potential heat stress based on temperature-humidity indices, manifesting in practically the entire area of Hungary. Thus, while crop ET rates and corresponding crop water use values (4989–5342 m3/ha) did not show substantial changes, maize WF in silage cultivation rose from 261.9 m3/t dry biomass in 2002 to 378.0 m3/t dry biomass in 2020. Feed and water intake was subsequently recorded on a cattle farm and assessed as green and blue water use. Drinking (blue) water uptake, ranging between 74.7 and 101.9 L/dairy cow/day, moderately correlated with temperature-humidity indices as heat stress indicators (r2 = 0.700–0.767, p &lt; 0.05). Servicing water was not recorded daily, but was calculated as a daily average (18 L/dairy cow/day), and was also considered in blue water usage. In contrast, feed consumption at the cattle farm corresponded to 13,352 ± 4724 L green water/dairy cow/day. Our results indicate that while the WF of animal feed remains a dominant factor in the total water use of dairy cattle farms, drinking water consumption and related costs of adaptive measures (such as adaptive breeding, modified housing, and technological measures) are expected to increase due to potential heat stress, particularly in selected regions where farmers should focus more on housing and technological solutions, as well as selecting for thermotolerance.

Assessing the impact of meteorological and agricultural drought on maize yields to optimize irrigation in Heilongjiang Province, China

Agriculture is an essential support for social and economic development, most affected by weather and climate. Drought in agriculture significantly threatens food security and social stability. Understanding drought variability in the cultivated land can lay the foundation for crop management. Considering the effects of drought on the actual crop growth process and precise irrigation under limited water resources is a great challenge. In this study, standardized precipitation evapotranspiration index (SPEI) and soil water content were used to study the spatiotemporal characteristics of drought during maize growth in Heilongjiang Province. The distributed AquaCrop model and grey theory were used to study the relationship between irrigation, drought and maize yield at the grid scale, and the current irrigation effect was evaluated by simulating the comparison of crop yield under non-irrigation and irrigated conditions, so as to provide a basis for optimizing the irrigation system in the future. In addition, Future drought trends were also examined using the Mann-Kendall statistical test, Sen's slope, and Hurst index. The results are as follows: (1) Based on remote sensing inversion and distributed AquaCrop model, the relationship between drought and crop yield can be studied at grid scale, which can refine the impact of drought on crop yield in different cells. (2) Irrigation can increase crop yields by at least 20–40% in about 60% of areas in the Heilongjiang Province. (3) In response to the drought characteristics of alternating wet and dry variations or consistent wet and dry variations in different months, inter-regional water transfer can be used to cope with or ensure increased irrigation in drought-prone months (May and June) to achieve water conservation and efficiency. Evaluating the current irrigation effect by simulating crop yield under non-irrigated conditions and crop yield under irrigated conditions can provide a better theoretical basis for optimizing irrigation systems, in order to improve regional crop yields and reduce drought risks.

The Use of Aquacrop Model for Soybean in Various Water Availability Within a Lysimeter System

The AquaCrop model is widely used under various agro-ecological conditions to reduce farm water consumption. The study aimed to simulate, validate, and measure the performance of AquaCrop models for canopy cover, biomass and soybean crop yields cultivated within a lysimeter. This research was conducted in the experimental field of the Faculty of Agriculture, the University of Jember, Indonesia (8°09'45.1" S, 113°42'58.2" E, 101 m a.s.l). There are four treatments in 4 lysimeters, namely P1 (irrigation based on recommendation), P2 (irrigation 95-105% FC), P3 (irrigation 75-85% FC) and P4 (irrigation 55-65% FC). The AquaCrop model is calibrated using canopy cover (CC) and then validated to predict the biomass and soybean yield. The experiment revealed that the model simulates better CC, biomass, and soybean yields with full irrigation than deficit irrigation. The performance of the AquaCrop model for soybeans of the Deja 2 variety in predicting CC, biomass, and soybean yield is impressive and reasonable. For the CC we found R2 ranges from 0.956 to 0.995, RMSE 10.389% to 3,293%, NRMSE 0.154% to 0.051%, NSE 0.918 to 0.992, and d 0.980 to 0.998. For biomass the R2 is 0.842, RMSE 0.111 t ha-1, NRMSE 0.017%, NSE 0.712, and d 0.937. For soybeans production the R2 is 0.999, RMSE 0.045 t.ha-1, NRMSE 0.018%,, NSE 0.908 and d 0.970. This study demonstrated that based on WUE, 55-65% FC irrigation is the most efficient application.

Optimization of virtual water flows in agriculture by changing cropping patterns using an integrated approach

Utilizing available water resources efficiently is crucial to address both our present and future requirements and plays a vital role in safeguarding food security. This current investigation deals with assessment and optimizing water footprint (WF) and virtual water flow (VWF) for primary crops in Banas River Basin (BRB) using AquaCrop model with local datasets and district-level estimates. VWF in the basins were estimated by multiplying the WF of crops with the amount exported/imported, which is determined based on the difference between production and consumption in the basin. The possibility of changing the cropping patterns was evaluated for the potential reduction of the blue WF. Annual WF from primary crops in the basin amounts to 19,255 MCM/yr (70 % green, 21 % blue and 10 % grey WF, respectively). Banas basin is a net exporter of agriculture commodities with nearly 7391 MCM/yr of water flowing out of the basin due to agricultural exports of which approximately 265 MCM/yr is virtual blue water outflow. Crops having low economic water productivity of blue water are being grown in vast areas resulting in a high blue WF. The optimizing the cropping pattern can result in a 5–42 % lower blue water footprint with 11–39 % higher economic output under different scenarios with and without considering the consumption needs. Changing the cropping pattern and making trade plan to optimize the crop import/exports can be viable option for tackling the blue water scarcity issues in the basin. WF can be managed sustainably by improving water resource allocation for better economic, social, and environmental productivity and going for less aggressive agricultural production.

Delineation of Rice Yield and Production in Combination of Crop Model and Remote Sensing

The study was to simulate rice yield in various places and outline a rice yield map for the study area using GIS, remote sensing, and a rice model. As a case study, the data were collected on the climate, soil characteristics, and rice cropping status in the AnGiang province (Southern of the Mekong River Delta in Viet Nam). The AquaCrop model was used to predict rice yield. The MODIS image delineated the rice cropping status based on spatial and temporal NDVI values. The results of the yield simulation are then put together with information about where the rice was planted, the weather, and the properties of the soil to make a map of the yield distribution. Finally, the outcomes are verified and contrasted with the statistical findings in the last step. The rice yield was predicted and compared with actual 1 and 6 percent rice yields. The anticipated rice yield map was established for the Winter-Spring cropping season 2012-2013 and the Summer-Autumn and Autumn–Winter cropping seasons 2013. Rice production and yield distribution can be divided into two major areas. The alluvial soil area produces significantly more rice than the LongXuyen quadrangle area because of the difference in soil and weather conditions. Rice yield simulation and delineation combining remote sensing and crop models is a good approach for yield prediction and better agricultural management strategy development in a country or region. The accuracy of the results depends on the quality of the input data, such as soil weather and remote sensing.&#x0D; Keywords: GIS, MODIS, cropping season, rice yield

Variance-based global sensitivity analysis and uncertainty quantification of the AquaCrop model parameters for basil (Ocimum basilicum L.) under different nitrogen fertilizer rates

Dynamic crop models are powerful tools for evaluating crop responses to alternative management options. These models consist of various input parameters that need calibration before being implemented in a new environment. Sensitivity analysis (SA) has proven to be an efficient method for identifying dominant model parameters. This study aimed to determine influential parameters of the AquaCrop model for basil and also quantify output uncertainty under different nitrogen fertilizer rates. The Sobol’ SA method was employed to compute both first-order and total-order sensitivity indices. Results indicated that normalized water productivity (WP*) and crop coefficient (KcTr,x) were the most influential parameters across all treatments. Furthermore, as fertility stress increased, the interaction effects among parameters decreased. The temporal analysis of model output (biomass) revealed that parameter sensitivities were highly time-dependent. In addition, the uncertainty of predicted biomass was determined using combined violin and box plots. Results demonstrated that the optimal nitrogen fertilizer application rate extended the distribution of model output. In summary, these findings offer valuable insights for model simplification and effective decision-making.

Optimizing crop production water footprints in the face of water scarcity: a combined experimental and simulation study of wheat in Zimbabwe

Abstract The optimization of water footprints in crop production is critical, given that agroecosystems currently account for more than 70% of global freshwater use. To achieve this, crop growth models provide insights into the impact of various crop and irrigation management strategies on both crop productivity and water use. This study evaluated the capability of the AquaCrop model in simulating wheat yields, crop water use, and water footprints in the Middle-Manyame Sub-Catchment, Zimbabwe. The model was calibrated and validated using experimental data collected from field experiments. Simulation experiments were conducted to assess the impact of early and late planting, drip and sprinkler irrigation techniques, and no mulch, organic mulch, and synthetic mulch options on the water footprint (WF). The AquaCrop model accurately simulated soil water content, crop water use, crop biomass, and grain yield. Simulation runs showed that early planting reduced WFblue and WFgreen by 25 and 4%, respectively. The lowest consumptive WF was observed with drip irrigation and synthetic mulching. The greatest decline in WFblue and WFgreen (52 and 11%) was simulated under early planting, using drip irrigation and synthetic mulching. Overall, the study highlights the importance of efficient crop and irrigation management practices to reduce water footprints in agroecosystems.

Simulation of saffron growth using AquaCrop model with high-resolution measured data

Saffron is one of the most expensive and worth-to-cultivate crops in countries struggling with water scarcity due to its low water requirement and compatibility with arid climates. This study was conducted to calibrate and validate the AquaCrop model for saffron at the research farm of the Ferdowsi University of Mashhad (FUM), Iran, in two growing seasons of 2021–2022 (two-year-old saffron) and 2022–2023 (three-year-old saffron). The soil water content, canopy cover, and biomass values were measured continuously during both growing seasons. The field measurements of the first and second growing seasons were used to calibrate and validate the AquaCrop model, respectively. Furthermore, using the Particle Swarm Optimization (PSO) algorithm, a new module for the inverse solution of the AquaCrop model has been presented. The module was successfully employed to fine-tune sensitive soil parameters of the field. The results showed that the AquaCrop model could accurately simulate soil water content, biomass, and canopy cover in both growing seasons. The root mean square error (RMSE) of simulations for soil water content, biomass, and canopy cover comparing to in-situ measured data was 8.3 mm, 0.3 ton ha−1 and 3.4% for the first year and 5.5 mm, 0.4 ton ha−1 and 2.7% for the second year, respectively. In these simulations, the saffron daughter corm production was considered as the crop yield, because the stigma yield is affected by the climatic conditions of the past year while corms are produced at the end of the prevailing season. To investigate the effect of cold and heat on saffron yield, the hottest and coldest growing seasons in the past 30 years were determined in 6 regions besides Mashhad, and the biomass in these seasons was simulated using the model. It was concluded that the cold growing season had a more significant effect than the hot season in reducing the saffron corm yield.

Enhancing state and parameter estimations of a dynamic crop model by a recombination particle filter

Crop simulation models are essential tools in supporting sustainable agricultural management. However, due to uncertainty in model parameters, the model predictions may not be sufficiently accurate. Data assimilation (DA) is a common approach to improve dynamic crop modeling by combining it with observation data. Among DA approaches, particle filter (PF) is a popular choice. Since conventional PF (CPF) may suffer from sample impoverishment problems, various filter modifications have been proposed in the literature, including the application of genetic operators (arithmetic cross-over and mutations). In this study, a novel PF approach inspired by the gene’s recombination process has been developed. In this new filter, named “recombination” PF (RPF), particle diversity is increased via information exchange between surviving particles and intermediate particles which are located close to existing particles. In turn, increased particle diversity reduces the chances of sample impoverishment and thus improves filter performance. The proposed method was tested on two synthetic study cases using the open-source AquaCrop model (v5.0a) and assuming weekly observations of canopy cover and soil water content. When CPF was implemented, the overall average normalized root mean square error (NRMSE), combining state and parameter estimations, and yield forecasts, all performed throughout the season, ranged from 4.0 to 5.1 % for ensemble sizes ranging from 150 to 500 particles. When RPF was implemented with a similar number of particles, the overall average NRMSE decreased to 3.6–3.7 %, corresponding to a 7–26 % improvement. Furthermore, higher stability of the results was observed, and the final parameter estimations improved in all the ensemble sizes investigated by approximately 40 %, which would be very beneficial for predicting crop growth in the next season.

A modeling framework to assess the crop production potential of green roofs

The increase in food demand and limited opportunities to expand agricultural land pose a threat to local and global food security. Producing food in urban areas such as green roofs can help satisfy urban food demand and thus alleviate pressure on agricultural land. However, a modeling framework that simulates crop growth and production potential on green roofs at a city scale is missing. Here, we adapt the Aquacrop model to explore the growth potential of various types of crops on green roofs and apply it to suitable roof areas in the city of Amsterdam. Our modeling framework includes irrigation methods for water use on green roofs that are optimized according to various climate-driven scenarios of water availability. We find that cabbage has the maximum achievable crop yields ranging from 30.8 to 75.9 t ha−1 yr−1, while pea has the minimum achievable crop yields ranging from 1.7 to 6.4 t ha−1 yr−1. The potential suitable green roof area (i.e., roofs with a certain slope and bearing capacity) for Amsterdam is roughly 400 ha for crop production. This represents 16 % of the total rooftop areas of Amsterdam and can produce up to a total of 28 kt of crops on an annual basis. Our modeling framework can be easily applied to other cities to identify the crop growth potential of green roofs. Our results can help policymakers and urban planners find optimal planting strategies and contribute to shorter food supply chains.

Assessment of soil mulching field management, and deficit irrigation effect on productivity of watermelon varieties, and AquaCrop model validation

Watermelon productivity in the Arba Minch irrigation scheme has been hampered by water scarcity, with only the Lady Bells watermelon variety being cultivated in the area. This challenge can be mitigated by adopting water-saving irrigation techniques and selecting water stress-resistant varieties. Hence, this study aimed to investigate the combined impact of soil mulching and deficit irrigation on the productivity of various watermelon varieties and validate the AquaCrop model. The experiment employed a randomized complete block design with two levels of water application (100 % SMD and 50 % SMD), two mulching practices (non-mulching and mulching), and four watermelon varieties: Lady Bells (V1), Green Pearl (V2), Kaolack (V3), and Koloss (V4). Soil physical properties and crop-related data were used to calibrate the AquaCrop model. Straw mulching, on average, conserved 64.50 mm and 262.75 mm of water under 100 % and 50 % water application levels, respectively. The minimum and maximum land productivity averages were 6.2 tonsha−1 (T13) and 17.6 tonsha−1 (T2), while water productivity ranged from 5.2 kgm−3 (T1) to 12.4 kgm−3 (T10). Lady Bell watermelon varieties displayed high sensitivity to water stress, with a 1.27 yield response factor under non-soil mulching treatment with 50 % water application. The mean benefit-cost ratio varied from 1.52 (T13) to 2.90 (T10). The average values of RMSE, NSE, and R2 for the AquaCrop model were 0.70, 0.65, and 0.80, respectively, indicating the model's acceptability in predicting the effects of mulching and deficit irrigation on watermelon productivity. Overall, the use of straw mulching combined with 50 % deficit irrigation, particularly for Green Pearl varieties, emerged as the most productive watermelon cultivation method in the Arba Minch region when facing limited irrigation water. Future research will focus on assessing the impact of deficit irrigation during various watermelon growth stages.

Crop monitoring and biomass estimation based on downscaled remote sensing data in AquaCrop model (case study: Qazvin Plain, Iran).

Ensuring food security requires monitoring crop growth conditions prior to harvesting. This research encompass three stages: remote sensing, crop modeling, and establishing a connection between the two. Besides, Fusion satellite images have been implemented to monitor and investigate fodder corn in three study areas. In the remote sensing stage, after implementing the downscaling algorithm and producing the leaf area index (LAI) time series, the results were compared with the estimated values from Landsat 8 and MODIS images, which were associated with overestimation in all cases. Furthermore, the results exhibited statistically significance with R2 > 95% and P-value < 0.05. The AquaCrop model was first calibrated and implemented in the crop modeling section at each growth stage based on the observational data measured in each field. The accuracy of the simulated model was checked according to the results of statistical indicators with high accuracy (NRMSE = 10% and RMSE = 0.03 (Ton/ha)) at significant level (95%) and was associated with underestimation. Using the SVM decision support algorithm, the relationship between downscaled LAI and calibrated CC (crop canopy) was estimated. This relationship was generated with 70% of the data and subsequently validated using the remaining 30% (R2 = 0.99, NRMSE = 0.01). On this basis, CC values were predicted. Finally, biomass values were compared with observed biomass values. According to the results of statistical indicators (RMSE = 0.19 (Ton/ha), NRMSE = 0.01, R2 = 0.96, P-value < 0.05), biomass estimation was highly accurate. These results demonstrate the reliability and accuracy of the model and the proposed method for simulating and estimating biomass before harvesting.

Irrigation scheduling for potatoes (Solanum tuberosum L.) under drip irrigation in an arid region using AquaCrop model.

Potato is one of the key food crops and China is the largest potato producer in the world. However water scarcity is the major constraint to increase the productivity of potato in the arid regions such as Ningxia in northwest China where this crop is extensively cultivated. The overall objective of this study was to optimize the irrigation for potato cultivated under the drip irrigation. To do this, the AquaCrop model was calibrated and validated using the data obtained from two years of field experiment. Then, the calibrated crop model was used to simulate growth and tuber yield of potato in response to 30 different irrigation schemes under two different irrigation scenarios. The crop model evaluation parameters namely, the root mean square error (RMSE), the index of agreement (d), the normalized root mean square error (NRMSE) and the coefficient of determination (R2) showed that the AquaCrop model could simulate the growth and yield of potato under the drip irrigation with different irrigation treatments with reasonable accuracy. Furthermore, yield of potato has increased with increasing amount of total irrigation under drip irrigation; however, yield begins to decline when the amount of total irrigation exceeds 2500 m3 ha-1. The study also found that the optimum irrigation schedule for potato was 20mm of irrigation quota at 7 days of irrigation cycle (i.e., 1800 m3 ha-1 or 180mm of total irrigation). The above irrigation scheduling has achieved 46.77t ha-1 of tuber yield with 15.74kg m-3 of water use efficiency. These findings may be evaluated in potato cultivation across different climate and soil conditions for wide applicability at different arid regions of the world.

Determination of the Most Efficient Forage Sorghum Irrigation Scheduling Strategies in the U.S. Central High Plains Using the AquaCrop Model and Field Experiments

The current status of water resources in the U.S. Central High Plains necessitates adopting water conservation practices to move toward a sustainable agricultural economy. Identifying proper irrigation scheduling techniques is a conservative practice to maintain the sustainability of the agricultural systems. However, conducting field experiments is time and money consuming. Thus, the utilization of crop models, such as AquaCrop, could be a convenient alternative to field experiments. The FAO AquaCrop model was calibrated and validated for simulating forage sorghum yield response to various deficit irrigation conditions in a semi-arid region. Afterwards, the model was used to investigate the efficiency of the pre-season and in-season irrigation scheduling scenarios. In this study, the soil water status at the planting time was considered as the indicator of the pre-season irrigation level. Therefore, the pre-season irrigation scenarios were arranged as the replenishment of soil water deficiency at the time of planting at up to 30, 50, and 100% of the soil’s total available water for the first 60 cm of soil depth and the same replenishment levels for the entire crop root zone (150 cm soil depth). Then, AquaCrop long-term (37 years) simulations of forage sorghum biomass and irrigation water use efficiency reactions to three levels of maximum allowable depletion (MAD) (40, 55, and 70%) were compared to three fixed irrigation interval (4, 6, and 10 days) scenarios by considering six pre-season irrigation conditions (36 scenarios). The scenarios analysis found the 10-day irrigation interval and the MAD levels of 55% and 70% to be the most efficient irrigation scheduling strategies if combined with pre-season irrigation that brought the crop root zone (0–150 cm soil depth) to field capacity. Moreover, the 40% MAD application was the least efficient strategy. This study’s outputs can be a baseline for establishing forage sorghum irrigation scheduling in the U.S. Central High Plains. However, exploring the interactions of irrigation scheduling strategies with other irrigation and agronomic practices, such as salinity management and fertilizer application, is highly recommended.

Using Film-Mulched Drip Irrigation to Improve the Irrigation Water Productivity of Cotton in the Tarim River Basin, Central Asia

Climate change has significantly influenced water resource patterns in arid regions. Applying effective water-saving measures to improve irrigation efficiency and evaluate their future water-saving capabilities is crucial for ensuring the sustainable development of irrigation agriculture. Based on the daily meteorological data from 15 global climate models (GCMs) in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this study used the AquaCrop model to perform high-resolution (0.1° × 0.1°) grid simulations of cotton yields and irrigation requirements. The study also investigated the ability of film-mulched drip irrigation (FMDI) to improve future irrigation efficiency under two shared socio-economic pathways (SSP245 and SSP585) in the Tarim River Basin (TRB), Central Asia, from 2025 to 2100. The results showed that the cotton yield and irrigation water productivity (WPI) in the TRB exhibited an upward trend of 13.82 kg/ha/decade (80.68 kg/ha/decade) and 0.015 kg/m3/decade (0.068 kg/m3/decade), respectively, during the study period. The cotton yield and WPI were higher in the northern, northwestern plains, and northeastern intermountain basin areas, where they reach over 4000 kg/ha and 0.8 kg/m3/decade. However, the cotton yield and WPI were lower in the southwestern part of the study area. Therefore, large-scale cotton production was not recommended there. Furthermore, compared to flood irrigation, the use of FMDI can, on average, improve the WPI by approx. 25% and reduce irrigation water requirements by more than 550 m3/ha. Therefore, using FMDI can save a substantial amount of irrigation water in cotton production, which is beneficial for improving irrigation efficiency and ensuring the future stable production of cotton in the TRB. The research results provide a scientific reference for the efficient utilization and management of water resources for cotton production in the TRB and in similar arid regions elsewhere in the world.

The modelling of tomato crop response to the climate change with different irrigation schemes

The inequality between available water supplies and growing water demand from diverse sectors, as well as the predicted climate changes are putting significant pressures on Egypt’s food security. There is a nation-wide demand for new scientifically proven on-farm practices to boost water productivity of major food crops. The objective of this study was to explore the use of various deficit irrigation schemes to improve water productivity ( WP) of tomato cultivated in Egypt under distinct climate change scenarios, RCP4.5 and RCP8.5, in three time-steps of the reference period (2006– 2016), 2030s, and 2050s. The AquaCrop model was used to simulate the influence of climate change on the tomato crop, as well as two deficit irrigation application schemes for the full growing season and the regulated application for the initial and maturity crop stages. With the same irrigation method, the predicted WP increased in a general pattern across all climate change scenarios. The combination of irrigation schedule with the 80% deficit irrigation can enhance WP near the optimum level (approximately 2.2 kg∙m–3), especially during early and mature stages of the crop, saving up to 16% of water. The results showed that the expected temperature rise by 2050s would reduce the crop growth cycle by 3– 11 days for all irrigation treatments, resulting in a 1–6% decrease in crop evapotranspiration ( ETc) and affecting the dry tomato yield with different patterns of increase and decrease due to climate change.

WHCrop: A novel water-heat driven crop model for estimating the spatiotemporal dynamics of crop growth for arid region

Crop models are widely used to assist in agricultural management decision-making and water productivity optimization. However, traditional crop models often depend on artificial and specific field management inputs, posing challenges in maintaining crops within a desired stress range. Consequently, the derived optimization schemes from these models become highly uncertain. Moreover, the complexity of the mechanisms involved and the multitude of parameters make it challenging to apply traditional crop models uniformly across various crops and regions. In this study, we have developed a novel crop model called WHCrop (Water-Heat Driven Crop model) that effectively captures, reflects, and controls the impact of various environmental factors (meteorology, topography, soil, and management) on crop growth process. The WHCrop model combines the simulation principles of biomass and yield from the CERES module in the DSSAT model, along with the soil water balance from the AquaCrop model, to estimate the dynamics of crop growth and production processes. Results indicated that WHCrop-based simulations, including canopy cover (CC), daily evapotranspiration (ET), and yield, matched well with ground-based measurements, and were better than the traditional crop model (DSSAT and AquaCrop) at both field and regional scales, especially under deficient irrigation conditions. Besides capturing the key variables associated with crop growth, WHCrop model could reproduce the adaptive response of these various to regional-scale temperature changes. Notably, the WHCrop model could effectively minimize uncertainties resulting from individual environmental change, thanks its incorporation of dynamic response mechanisms for crop growth under stress factors. Overall, the novel and informative WHCrop model offers some advantages over traditional crop models since it allows for optimal decision making to be derived from the randomly different inputs. As a result, the WHCrop model proves instrumental in assisting decision-makers in formulating critical water allocation strategies and developing effective management recommendations to enhance regional agricultural water productivity.

Assessment of growth and productivity of pearl millet (Pennisetum glaucum L.) with varied sowing environments and nitrogen concentrations using AquaCrop model

Assessment of growth and productivity of pearl millet (Pennisetum glaucum L.) with varied sowing environments and nitrogen concentrations using AquaCrop model

Performance of AquaCrop model for predicting yield and biomass of okra (Abelmoschus esculentus) crop

The present study was carried out at Technology Park of College of Technology and Engineering, Udaipur, Rajasthan for two years (2019 and 2020) with 6 irrigation treatments and four replications in RBD. Among all the treatments, the treatment T2 (Irrigation at 85% field capacity of soil, based on soil moisture sensor based drip irrigation system) was found best in selected areas for growing okra [Abelmoschus esculentus (L.) Moench] crop under sensor based drip irrigation with maximum crop yield and biomass. In other water scarce regions of Rajasthan, the use of a simulation model can be a better option to predict the crop yield and biomass with respect to different irrigation levels. Therefore, calibration and validation of the AquaCrop model was done with the help of various data which were collected from field experiments during the study period (2019–20). The cut off temperature and base temperaturewere set as 32.5–100C, respectively. The model was successfully calibrated and validated with minimum prediction error, mean absolute error (MAE: 0.34–0.56), root mean square error (RMSE: 0.38–0.60) and maximum model efficiency (E ranges 0.8–0.95) and coefficient of correlation (R2 &gt; 0.9). The overall result indicate good performance of the model and can help in decision making as well as for irrigation water management for okra crop under the given conditions of Udaipur district of Rajasthan. It can be used to assess the influence of various environmental factors and management practices on okra crop growth.