AquaCrop Model Research Articles

Evaluating the impact of climate change on yield and water use efficiency of different dry-season rice varieties cultivated under conventional and alternate wetting and drying conditions.

This study is the first attempt to assess rice cultivation under alternate wetting and drying (AWD) and continuous flooding (CF) using the latest scenarios from the Intergovernmental Panel on Climate Change(IPCC), utilizing AquaCrop Model. Field experiments were conducted during the dry season 2023 to get the model calibration and validation input. We used two shared socioeconomic pathways scenarios (SSP3-7.0 and SSP5-8.5) developed within Coupled Model Intercomparison Project Phase 6 (CMIP6) and projected the rice growth during 2040-2070. The simulation results demonstrated the effectiveness of AquaCrop in capturing crop development across treatments and varieties. This model's accuracy in simulating canopy cover (nRMSE = 14-32.5%), time series biomass (nRMSE = 22-42.5%), grain yield (Pd = 4.36-24.38%), and total biomass (nRMSE = 0.39-18.98%) was generally acceptable. The analysis of future climate shows an increasing trend in the monthly average temperature by 0.8°C (Tmin) and 1.3°C (Tmax) in both scenarios. While ETo values were not anticipated, rainfall was expected to increase with average values of 5.62mm to 11.25mm. In addition, the study found that varieties with growing periods longer than 93days after transplanting (DAT), such as CAR15 and Sen Kra Ob, were most impacted by heat stress conditions, leading to reduced yield, harvest index (HI), and water use efficiency (WUE). In our case, CAR15 and Sen Kra Ob grain yields were reduced by 53% and 8%, respectively. AWD maintains superior WUE compared with CF regardless of the type of varieties, suggesting this technique is a drought-adaptive strategy.

Life cycle assessment and synergistic effects of sustainable strategies to improve environmental sustainability in arid climates

Adapting sowing date (SD) based on environmental sustainability and increasing productivity is crucial for creating flexibility in agriculture and maintaining food security. This study aimed to optimize SD and nitrogen (N) management using environmental indices in an arid climate. Yield, N use efficiency (NUE), water productivity (WP), and total environmental impact (EI) were assessed for five SDs for barley and triticale crops, along with three levels of N fertilizer application. The AquaCrop model was used for crop yield prediction, and EI was evaluated using Life Cycle Assessment (LCA). Delayed SD led to a shorter growing period (10.6–12.8 %), decreased WP (76.4–87.5 %), and decreased yield (58.3–87.6 %) for both crops compared to conventional SD. In contrast, earlier SD resulted in an extended growing period (1.7–3.6 %), increased WP (14.8–20.4 %), and increased yield (8.3–10.2 %). NUE showed an inverse relationship with increasing fertilizer levels. The most significant EIs were due to N consumption (33.4 %), followed by diesel (28.5 %) and potassium fertilizer (13.7 %) for both crops. Early SD could reduce EI by an average of 12.5 %. Combining early SD with moderate N levels was the most sustainable approach to maximize yield, minimize water use, and reduce EI, thereby informing sustainable practices for cereal cultivation in similar climates.

Maize biomass yield variations due to diverse nitrogen applications using Aquacrop

AbstractIn the quest to increase crop yields and production intensity to meet the demands of a growing global population, the responsible use of nitrogen (N) applications is paramount. Excessive application of these fertilizers results in economic losses, environmental pollution and reduced N‐use efficiency. To address this challenge, precision agriculture techniques offer promising solutions. This study explored the relationships among applied N, maize crop parameters and the nitrogen nutrition index (NNI) to optimize N application usage and crop performance. The AquaCrop model was used to simulate water productivity and plant parameters accurately. The research establishes equations between the NNI and maize parameters, enabling the use of AquaCrop to simulate crop responses to varying N levels. The results demonstrate that AquaCrop effectively simulates more than 95% of the biomass changes. The findings suggest that applying N based on presented equations optimized the use of N applications, thereby mitigating economic losses and environmental impacts.

Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency

This study presents a Reinforcement Learning-based algorithm designed to optimise irrigation for Durio Zibethinus (i.e., durian) trees, aiming to maximise tree growth and reduce water usage. Traditional irrigation methods, as well as current machine learning models, often focus only on soil moisture and weather data, neglecting critical factors like actual tree growth. This study proposed a reinforcement learning irrigation (RL-Irr) algorithm incorporating tree growth stages, soil moisture, and weather conditions to determine precise irrigation needs. The algorithm was developed by calibrating the AQUACROP model using data from actual durian plantations where rain-fed irrigation (rain-fed) was practised. Daily irrigation volumes were calculated based on real-time soil moisture, weather forecasts, and weekly tree growth measurements. The reinforcement learning method was used to optimise irrigation schedules, with rewards based on soil moisture, tree growth, rainfall, and weather conditions. The algorithm was tested using AQUACROP simulations and compared against soil moisture balance irrigation (SMB-Irr) and rain-fed. The results showed that the RL-Irr reduced water use by up to 75 percent while maintaining tree growth. These findings suggest the algorithm could significantly improve water efficiency in durian farming, though real-world applications should consider potential model limitations.

Water Use Efficiency in Rice Under Alternative Wetting and Drying Technique Using Energy Balance Model with UAV Information and AquaCrop in Lambayeque, Peru

In the context of global warming, rising air temperatures are increasing evapotranspiration (ETc) in all agricultural crops, including rice, a staple food worldwide. Simultaneously, the occurrence of droughts is reducing water availability, affecting traditional irrigation methods for rice cultivation (flood irrigation). The objective of this study was to determine ETc (water use) and yield performance in rice crop under different irrigation regimes: treatments with continuous flood irrigation (CF) and irrigations with alternating wetting and drying (AWD5, AWD10, and AWD20) in an experimental area in INIA–Vista Florida. Water balance, rice physiological data, and yield were measured in the field, and local weather data and thermal and multispectral images were collected with a meteorological station and a UAV (a total of 13 flights). ETc values obtained by applying the METRICTM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) energy balance model ranged from 2.4 to 8.9 mm d−1 for the AWD and CF irrigation regimes. In addition, ETc was estimated by a water balance using the AquaCrop model, previously parameterized with RGB image data and field weather data, soil, irrigation water, and crops, obtaining values between 4.3 and 7.1 mm d−1 for the AWD and CF irrigation regimes. The results indicated that AWD irrigation allows for water savings of 27 to 28%, although it entails a yield reduction of from 2 to 15%, which translates into an increase in water use efficiency (WUE) of from 18 to 36%, allowing for optimizing water use and improving irrigation management.

Enhancing agricultural sustainability with water and crop management strategies in modern irrigation and drainage networks

Improving global food security hinges on achieving sustainable agricultural production within independent irrigation and drainage units, considering the availability of sustainable water resources. This study investigated the potential for enhancing the sustainability of agricultural production in a modern irrigation and drainage network (TIDN) area and evaluated the network's performance under various strategies. These strategies included mulching, transitioning from traditional to drip irrigation, combining mulching with drip irrigation (IDM), and reducing the crop yield gap. Utilizing 2009–2018 data and the AquaCrop model, which was both calibrated and validated, the green and blue water footprints (GWF and BWF) of dominant crops in the region's cropping pattern were assessed. The crop cultivation was prioritized based on the total and unit unsustainable BWF. The performance of TIDN was evaluated using indicators of reliability, vulnerability, system deficiency, flexibility, and sustainability. The drip irrigation had the most significant impact on reducing BWF, while mulching was more effective in decreasing GWF. The adoption of IDM led to a total annual water savings of 382.4 m3 ha−1. The 10-year average reductions in BWF from May to August ranged from 1.7 to 3.1 MCM, with approximately 88–93 % of these savings attributed to decreased water consumption in rice fields. Mulch, drip, and IDM decreased the BWF by 3.3 %, 9.4 %, and 10.2 %, respectively, compared to current conditions. The study revealed that the expansion of rice cultivation under conventional flooding irrigation was incompatible with the sustainable blue water resources available, and replacing rice with wheat could reduce the unsustainable BWF by 95–100 %. Compared to current conditions, IDM improved network sustainability by 1.2 %, while closing the yield gap improved the indicator by 24 %. The findings suggest that combining infrastructural solutions, such as updating irrigation systems, with farm management solutions, such as reducing crop yield gaps and implementing mulching, can significantly enhance the sustainability of production in intensively agricultural areas.

Computation of evapotranspiration using crop simulation models and comparison with leaf area index from multiple sources

The study demonstrated the computation of evapotranspiration (ET) in cultivation of groundnut (Arachis hypogaea) through the application of crop simulation models, alongside a comparative analysis with leaf area index (LAI) from various sources. The cultivation period for groundnut was conducted during the calendar year 2023, during which 2 distinct growth patterns were noted, attributed to variations in environmental conditions. The study analyses the estimated evapotranspiration from AquaCrop model, utilizing crop coefficient (Kc) values in comparison with evapotranspiration data derived from satellite observations provided by the MOD16A2v061 product. Furthermore, LAI was measured through 3 methodologies: an empirical equation based on field data, the Li-Cor 2200 plant canopy analyzer and LAI calculations derived from cloud-free normalized difference vegetation index (NDVI). LAI obtained from Li-Cor 2200 instrument exhibited a higher degree of consistency in correlation with empirical LAI derived from ground observations. Conversely, LAI values calculated using the normalized difference vegetation index (NDVI) demonstrated a greater degree of variability, especially during times of cloud cover. The study emphasizes the relationship between LAI and ET and magnitude of LAI in amount of total evapotranspiration.

Long-term investigation of the irrigation intervals and supplementary irrigation strategies effects on winter wheat in the U.S. Central High Plains based on a combination of crop modeling and field studies

Implementing optimized irrigation strategies to achieve acceptable wheat yield while conserving groundwater resources is critical for extensively irrigated regions. Field experiments regarding winter wheat irrigation management were conducted for two growing seasons (2013–2014 and 2014–2015) to calibrate and validate the AquaCrop model. To determine proper parameterization of the AquaCrop model for various data availability conditions, the model performance was tested for calibration based on three different datasets, including a) calibration based on a full irrigation condition (CA 1), b) calibration based on a dryland condition (CA 2), and c) calibration based on diverse irrigation conditions, which included full irrigation, different deficit irrigation levels, and dryland (CA 3). The CA 3 calibration scenario resulted in the model's highest accuracy in simulating biomass, grain yield, total soil water, and seasonal evapotranspiration during the calibration and validation process. However, the model performance was also convenient when calibration based on full irrigation conditions was pursued. The calibrated and validated AquaCrop model based on the CA3 was used to analyze long-term (36-year) irrigation management scenarios for identifying the optimized water-conservative winter wheat irrigation strategies. The long-term analysis emphasized that increasing irrigation intervals from 7 to 15 or 20 days could reduce groundwater withdrawal by 52–64 % while expecting a 14–19 % reduction in grain yield. By implementing one 25 mm irrigation after planting and two 25 mm depth irrigation events at jointing or leaf growth stages, 91 % of Kansas's average winter wheat yield (2.88 tons/ha) could be achieved. During wet years, the 1.2 ton/ha reduction in biomass and 0.27–0.62 ton/ha reduction in grain yield is expected irrespective of irrigation management strategies. Considerable uncertainty was detected in grain yield production during dry years under dryland (rainfed) conditions. The maximum winter wheat production could be achieved under normal years, establishing a balance between precipitation and heat units. Our agro-hydrological analysis revealed that the highest irrigation water use efficiency could be attained by the application of two 25 mm irrigation events at jointing or flag leaf growth stages during normal years, the application of two irrigation events during jointing or heading during wet years, and two 25 mm irrigations at heading or flowering during dry years. The results of this research could be used as a baseline for producers of the U.S. Central High Plains and semi-arid regions with similar climate characteristics to cope with water scarcity in winter wheat production.

Application of the AquaCrop model and response surface method to determine the optimum irrigation water and seeding density for wheat growing under sprinkler irrigation system

AbstractThis study aimed to determine the optimal seeding density and irrigation water for maximizing wheat yield, water productivity and income using the AquaCrop model and response surface method (RSM). The AquaCrop model was calibrated and validated using experimental data from two successive growing seasons. The AquaCrop simulations for grain yield, biomass, soil water content, evapotranspiration and canopy cover were satisfactory and acceptable. After calibration, the model simulated the grain yield for 47 years with five seeding densities and four irrigation schedules. By applying RSM to the AquaCrop model outputs over 47 years, it was found that irrigation water amounts of 747, 198 and 747 mm, along with seeding densities of 211, 188 and 208 kg ha−1, respectively, maximized the wheat yield, water productivity and profit per unit area. A seeding density of 162.4 kg ha−1 combined with a total of 350 mm of irrigation water and rainfall resulted in the highest water productivity. This study demonstrated the reliability of the AquaCrop model in predicting wheat grain yield under various irrigation and seeding density conditions, providing valuable insights for farmers and agricultural experts to optimize crop productivity and profitability.

Using a Triple Sensor Collocation Approach to Evaluate Small-Holder Irrigation Scheme Performances in Northern Ethiopia

This study uses a triple-sensor collocation approach to evaluate the performance of small-holder irrigation schemes in the Zamra catchment of Northern Ethiopia. Crop water productivity (CWP), as an integrator of biomass production and water use, was used to compare the overall efficiencies of three types of irrigation systems: traditional and modern diversions, and dam-based irrigation water supply. Farmer-reported data often rely on observations, which can introduce human estimation and measurement errors. As a result, the evaluation of irrigation scheme performance has frequently been insufficient to fully explain crop water productivity. To overcome the challenges of using one single estimation method, we used a triple-sensor collocation approach to evaluate the efficiency of three small-scale irrigation schemes, using water productivity as an indicator. It employed three independent methods: remotely sensed data, a model-based approach, and farmer in-situ estimates to assess crop yields and water consumption. To implement the triple collocation appraisal, we first applied three independent evaluation methods, i.e., remotely sensed, model-based, and farmer in-situ estimates of crop yields and water consumption, to assess the crop water productivities of the systems. Triple-sensor collocation allows for the appraisal and comparison of estimation errors of measurement sensor systems, and enables the ranking of the estimators by their quality to represent the de-facto unknown true value, in our case: crop yields, water use, and its ratio CWP, in small-holder irrigated agriculture. The study entailed four main components: (1) collecting in-situ information and data from small-holder farmers on crop yields and water use; (2) derivation of remote sensing-based CWP from the FAO WaPOR open database and time series; (3) evaluation of biomass, crop yields and water use (evapotranspiration) using the AquaCrop model, integrating climate, soil data, and irrigation management practices; (4) performing and analysis of a categorical triple collocation analysis of the independent estimator data and performance ranking of the three sensing and small-holder irrigation systems. Maize and vegetables were used as main crops during three consecutive irrigation seasons (2017/18, 2018/19, 2019/20). Civil war prevented further field surveying, in-situ research, and data collection. The results indicate that remote sensing products are performed best in the modern and dam irrigation schemes for maize. For vegetables, AquaCrop performed best in the dam irrigation scheme.

Calibration and validation of the AquaCrop model for forage cactus production systems under different management interventions in the semi-arid region of Brazil

Simulation models are useful tools for estimating plant response and contributing to the development of management strategies and yield predictions in crops. This study aimed to calibrate and evaluate the AquaCrop-FAO model for the forage cactus imposed on different cropping systems in the semi-arid region of Brazil. Four experiments were conducted: cactus-sorghum intercropping system with five spacings between plants (0.10, 0.20, 0.30, 0.40 and 0.50 m); cactus-sorghum intercropping system with five spacings between rows (1.00, 1.25, 1.50 and 1.75 m); single cactus with four levels of mulch (0, 5, 10 and 15 Mg ha−1); and single forage cactus with four levels of nitrogen fertilisation (50, 150, 300 and 450 kg of N ha−1). The performance of the model was evaluated using the following parameters: root mean squared error (RMSE); normalised RMSE (NRMSE), coefficients of determination (R2), Nash-Sutcliffe model efficiency coefficient (ME) and the Willmott index of agreement (d). During the calibration stage, the statistical indices pointed to the good performance of the AquaCrop model in estimating dry biomass in most of the cropping systems, with 10 < NRMSE <20; R2 > 0.96 and ME > 0.95. The AquaCrop model showed the best performance simulating systems under different levels of nitrogen fertilisation. Among the intercropping systems, the denser arrangements showed the highest values for simulated biomass.

Determining clogging threshold of ceramic emitters to promote wolfberry production in Northwest China

Drip irrigation is a worldwide water-saving irrigation technology and benefits crop production. Yet, the emitter clogging is still a crucial problem for application. Recently, ceramic emitter (CE) with porous flow path was proposed as an alternative to the plastic drip emitter for continuous irrigation. However, there is still a gap in the estimation of the CE clogging degree. To address this, we conducted the field experiment in Tongxin, Yinchuan and Qinghai with the aim of the determination of the suitable discharge ratio variation (Dra) threshold of CEs under different climate conditions by using the AquaCrop model and segmented linear regressions. The results showed that there was a determined Dra threshold for soil water content (SWC), wolfberry canopy cover, biomass and yield in three experimental sites by 53.55 % - 54.62 %, 57.11 % - 65.94 %, 55.58 % - 64.30 % and 51.76 % - 61.23 %, respectively. Moreover, the Dra thresholds decreased with the precipitation increased in different climate conditions. Furthermore, we determined the Dra threshold of CEs was 56.1±5.3 % for wolfberry production, which was remarkably lower than the international standard of emitter clogging level by 75 %. Our study provides scientific insight to determine the clogging thresholds of CEs and further generalizes this technique in the irrigation science area.

Combining mathematical models and machine learning algorithms to predict the future regional-scale actual transpiration by maize

Plants on the land surface play a vital role in the hydrological water cycle as they transport soil water to the atmosphere through transpiration. Root water uptake (RWU) is considered a crucial step in this process as it is the first stage of transpiration, directly determining the actual transpiration (Ta) of plants. However, accurately measuring RWU or Ta in situ poses significant challenges. Here, we establish an overall approach of combining mathematical models and machine learning algorithms to obtain high-precision (500 m×500 m) regional-scale daily Ta maps for various future climate patterns. The Hydrus-1D and AquaCrop models were employed to calculate the total RWU fluxes across the entire root zone, aiming to achieve Ta at a point scale. A machine learning model was developed using the CatBoost algorithm and environmental covariates extracted from the Google Earth Engine (GEE) platform to upscale these point-scale Ta to the regional scale. Furthermore, a total of 22 CMIP6 Earth System Models (ESMs) were evaluated, and among them, ACCESS-CM2 and ACCESS-ESM1–5 were selected for simulating future climate scenarios. Based on the established machine learning model and selected ESMs, regional-scale Ta maps were generated from 2020 to 2100 for the SSP245 and SSP585 (Shared Socioeconomic Pathways) scenarios. The results indicate that near-surface specific humidity, mean near-surface air temperature, latitude, and surface downwelling shortwave radiation are the critical factors influencing regional-scale Ta. As greenhouse gas emissions intensify and temperatures rise, regional-scale Ta is enhanced, leading to an accelerated transfer of soil water to atmospheric water. Under the SSP245 scenario, Ta increases on average by 0.55–1.16 % every 20 years, with its incremental value ranging from 7.14∙10−4 to 8.65∙10−4 cm day−1, while under the SSP585 scenario, Ta increases more significantly, achieving an average increase of 0.64–1.81 % every 20 years, with its incremental value ranging from 1.595∙10−3 to 2.821∙10−3 cm day−1. This study provides a robust integrated approach to assess the future regional-scale Ta providing valuable insights into the underlying water cycle mechanisms and regional water requirements for future climate scenarios.

Determination of yield response character function for Broccoli: A case study for Ayodhya region using FAO aqua crop model

Determination of yield response character function for Broccoli: A case study for Ayodhya region using FAO aqua crop model

Simulation of Maize Growth Under the Applications of Brackish Water in Northwest China

The objective of this study is to assess the suitability of the AquaCrop model for growing maize using brackish water irrigation in Northwest China. Additionally, this study aims to examine how maize utilizes water in various soil layers when irrigated with varying water qualities. The AquaCrop model was calibrated and verified using experimental data from the years 2022 and 2023 in this research. (1) The findings indicated that the AquaCrop model effectively simulated the canopy cover, biomass, and yield of maize when irrigated with brackish water. The validation year’s R2, MAPE, and RMSE values for canopy cover, biomass, and yield of maize were 0.95, 5.36%, and 4.77%, respectively. For biomass, the R2, MAPE, and RMSE values were 0.91, 16.61%, and 2.12 t·hm−2, respectively. For yield, the R2, MAPE, and RMSE values were 0.84, 3.62%, and 0.42 t·hm−2, respectively. (2) Irrigation with water of high mineral content, measured at 1.6 ds/m, as well as with fresh water over the whole reproductive period, resulted in an increased reliance on groundwater for maize cultivation. There was no notable disparity in the usage of various soil layers between the irrigation with alternating freshwater and brackish water. (3) The AquaCrop model simulated the effects of seven different irrigation water quality treatments. It was shown that using water with mineralization levels of 0.5 and 0.8 ds/m resulted in decreased freshwater use without causing a substantial decrease in maize yield and biomass.

Analysis of Maize Planting Mode and Simulation and Optimization of Ridging and Fertilization Components in Arid Area of Northwest China

The arid area of Northwest China belongs to the rain-fed agricultural area of the Loess Plateau, and water resources have become one of the important factors limiting agricultural development in this area. This study employed the AquaCrop model to predict the yield advantages and environmental adaptability of maize in Dingxi City from 2016 to 2020 under two cultivation practices: ridge tillage (100% film coverage with double ridge-furrow planting) and flat planting (81.8% film coverage with wide-film planting). The numerical simulation of the tillage and fertilization process of the double-ridge seedbed was carried out by EDEM, and the key components were tested by the Box–Behnken center combination test design principle to obtain the optimal parameter combination. The results showed that ridge planting was more suitable for agricultural planting in rain-fed arid areas in Northwest China. The simulation analysis of ridging and fertilization showed that the forward speed of the combined machine was 0.50 m/s, the rotation speed of the trough wheel of the fertilizer discharger was 39 rmp, and the rotary tillage depth was 150 mm. The qualified rate of seedbed tillage was 93.6%, and the qualified rate of fertilization was 92.1%. The research shows that the whole-film double ridge-furrow sowing technology of maize is more suitable for the rain-fed agricultural area in the arid area of Northwest China. The simulation results of the ridging fertilization device are consistent with the field experiment results. The research results provide a certain technical reference for the optimization of the whole-film double ridge-furrow sowing technology.

Modeling the response of sesame (Sesamum indicum L.) to different soil fertility levels under rain-fed conditions in the semi-arid areas of western Tigray, Ethiopia

Sesame, a crucial oilseed crop in Ethiopia, ranks second only to coffee in its importance as an exported agricultural commodity. However, inadequate soil fertility management has hampered its productivity despite its substantial international market demand. Hence, this study was conducted to model the response of sesame to different nitrogen fertilizer levels using the AquaCrop model, and to assess the capability of the model as a decision-support tool for optimizing soil fertility management strategies in the study area. The experiment was laid out in a randomized complete block design, consisting of four nitrogen fertilizer rates (0, 23, 46, and 69 kg/ha nitrogen) and three distinct sesame varieties (Setit-1, Setit-2, and Humera-1). Over the course of the three cropping seasons, data on soil physical and chemical properties, crop growth, yield and yield components were collected for each treatment. Evaluation of model performance relied upon established metrics of coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (N-RMSE), model efficiency (E), and degree of agreement (D). Analysis of results revealed the AquaCrop model appropriately calibrated for simulation of soil water content, showing R2 values ranging from 0.92 to 0.98, RMSE values varying from 6.5 to 13.9 mm, E values from 0.78 to 0.94, and D values from 0.95 to 0.99. Similarly, simulation outputs for aboveground biomass (AB) demonstrated good accuracy of the model, with R2 values varying from 0.92 to 0.98, RMSE values ranging from 0.33 to 0.54 tons/ha, and D values from 0.9 to 0.98. Notable accuracy was also observed in the simulation of canopy cover (CC), revealing R2 values between 0.95 and 0.99, and RMSE values ranging from 5.3 to 8.6 %. In conclusion, this study substantiates the successful calibration and validation of the AquaCrop model for predicting sesame response to diverse nitrogen fertilizer levels. The performance of the model in predicting soil water content, CC, AB, and yield highlights its potential as a valuable tool for optimizing soil fertility management and enhancing sesame cultivation practices in Ethiopia.

An integrated approach to obtain high-precision regional root water uptake maps

Root water uptake (RWU) is exceptionally difficult to determine in situ due to its unique nature of occurring underground. Accurately characterizing the spatial–temporal characteristics of RWU at a regional scale remains a significant challenge, and high-precision regional-scale RWU maps have not yet been reported. In this study, we develop a novel approach that integrates the Google Earth Engine (GEE) platform, mathematical models, and machine learning algorithms to generate high-precision regional maps of maize RWU in China’s primary maize production area. The GEE platform was used to extract essential parameters such as soil properties, leaf area index, and growth period. These variables, along with meteorological data from 122 agricultural meteorological stations, were used to calculate RWU at a point scale using the Hydrus-1D and AquaCrop models. Finally, we utilized Hydrus-calculated point-scale RWU fluxes, along with 31 covariates extracted from the GEE platform, in conjunction with the CatBoost algorithm to develop a machine-learning model for estimating regional-scale RWU. The accuracy of the developed machine-learning model was validated using statistical measures, such asa mean absolute error (0.0299 cm d−1), a root mean squared error (0.0398 cm d−1), and the correlation coefficient (0.9178). The model was then applied to generate high-precision (500 m × 500 m) regional-scale RWU maps from 2015 to 2019. Additionally, our analysis identified several parameters, including TempDew (i.e., 2-m dewpoint temperature), SurfaceSM (i.e., top layer soil moisture), TempMax (i.e., max 2-m air temperature), and Latitude, as key factors influencing regional-scale RWU. This approach provides a robust strategy for accurately generating regional-scale RWU maps, even with limited data. Furthermore, it can be extended to obtain other regional indices, offering valuable insights for various real-world applications.

Simulation of water content in the soil, canopy growth and biomass production for the maize crop, in different conditions of water replenishment with the aquacrop model

Simulation models for crop production are tools increasingly used to aid decision making and resource optimization, such as irrigation. In this sense, this study aimed to evaluate the performance of the AquaCrop model in the simulation of water content in the soil, canopy growth and biomass production for the maize crop under different water replenishment conditions, in the western border region in the State of Rio Grande do Sul - Brazil. It was used data from climate, soil, crop and management from two experiments developed in the 2017/18 and 2018/19 seasons in the municipality of Alegrete. The irrigation treatments corresponded to layers of 0, 50, 75, 100 and 125% of ETc for the first cultivation period, and 0%; 50, 75 and 100%, suppressed by 50% in the reproductive period of the crop; and 100% of ETc for the second cultivation period. The performance index, used in the evaluation, classified the AquaCrop model as “excellent” for the simulation of canopy cover and biomass production, and “very good” for the simulation of water content in the soil. The model presented a tendency to underestimate canopy coverage under deficit conditions and to overestimate it under irrigation conditions. For the production of biomass and water content in the soil, the model tended to overestimate the results of treatments under conditions of water deficit.

Enhancing climate resilience in northern Ghana: A stochastic dominance analysis of risk-efficient climate-smart technologies for smallholder farmers

Northern Ghana is a semi-arid region characterised by a unimodal rainfall pattern, and hot and dry weather conditions. Heavy reliance on rain-fed agriculture and the lack of resources for irrigation, makes smallholder farmers in the region increasingly vulnerable to climate-related crop failures. In recent years, climate-smart technologies (CSTs) such as changing planting dates (PD), compartmental bunding (CB), mulching (M), and transplanting (TP) have been recommended to minimise yield losses. However, there is limited information on the most risk-efficient CSTs for crops cultivated in the region. This study used a stochastic dominance approach to identify the most risk-efficient CSTs for maize, rice, and sorghum. The stochastic modelling process employed the Aqua-crop model to simulate climate-related yield variability using Ghana climate data, and gross margin variability with crop budgets from literature sources. From the study's findings, changing planting date from April to May was the most risk-efficient choice for maize and sorghum under farmers' and recommended practices. In contrast, transplanting was the most risk-efficient technology for rice farming in the study area. The study also highlights the importance of considering the risk-averse nature of smallholder farmers when selecting CSTs. By identifying the most risk-efficient CSTs, the study can help improve the resilience of smallholder farmers. These findings have important implications for the development and adoption of CSTs in northern Ghana.