AquaCrop Model Research Articles

Using the AquaCrop model to assess the cotton yield response to three irrigation schedules in the Río Dulce Irrigation System, Santiago del Estero, Argentina

This work evaluates the cotton response to irrigation scheduling using AquaCrop, in the Río Dulce Irrigation System (SRRD), Santiago del Estero, Argentina. The model was calibrated and validated to simulate the cotton´s growth and yield for the SRRD, where most of the cotton is grown in a cropping system called narrow rows (0.52 to 0.76 meter between rows, 200,000 to 220,000 plants per hectare). The model adaptation to different cultivars and agronomical practices was noteworthy. Then, the impact of three different irrigation schedules on cotton production was assessed using a series of 35 years of daily climatic data. The irrigation scenarios were defined based on the farmers’ practices and on the rotational water delivery of the SRRD. The highest yields were attained when irrigation was applied at 25 and 55 days after sowing (DAS), followed by 55 DAS, and, finally, 55 and 85 DAS. Considering both the yields and the water use, irrigating at 25 and 55 DAS would be the best option for a normal season in the SRRD. This work shows the usefulness of combining the use of crop simulation models, field measurements and long-term weather data to analyze yield trends and irrigation water use under different scenarios.

Evaluating the Performance of the AquaCrop Model to Soil Salinity in Jordan Valley

The demand to apply a decision support system to simulate salinity and drought is increasing with time, particularly in arid and semi-arid regions like Jordan, where the threat of land degradation by salinization is of high concern. The main objective of this research was to evaluate the response of the AquaCrop model to soil salinity in Jordan Valley. Two experiments were conducted, one at the field and the other at the greenhouse. Three water salinity levels (S): S1 (control = 2 dS m−1), S2 (4 dS m−1), and S3 (8 dS m−1) with three irrigation amounts (R): R1 (control = 120%), R2 (100%), and R3 (70%) were used in the field. Four levels of saline water (S):S1 (control =0.65), (4) S2, (8) S3, and (10) dS m-1) S4 were used in the greenhouse. In both experiments, grain yield, and aboveground biomass were measured after harvesting. Soil salinity and pH were measured every three weeks during the growing season to monitor soil salinization. Results showed that the final field grain yield was good in calibration and validation, with a 0.96 agreement index (d). The efficiency factor (E) was 0.86 and 0.87 for calibration and validation, respectively, while the normalized root mean square error (NRMSE) was less than 4 %. Field biomass d-index of 0.87 and 0.71 and E of 0.65 and 0.45 for Calibration and Validation were found, respectively. In the greenhouse experiment, the results were less satisfactory. Grain yield showed d-index of 0.84 and 0.88 in calibration and validation, respectively, while biomass showed poor results. All statistical criteria used in this research indicated that the model can simulate grain yield and biomass properly in the field, however, biomass statistical results were less accurate. Overall it is recommended, to use AquaCrop for soil salinity management in Jordan Valley.

Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance

Sensor data and agro-hydrological modeling have been combined to improve irrigation management. Crop water models simulating crop growth and production in response to the soil-water environment need to be parsimonious in terms of structure, inputs and parameters to be applied in data scarce regions. Irrigation management using soil moisture sensors requires them to be site-calibrated, low-cost, and maintainable. Therefore, there is a need for parsimonious crop modeling combined with low-cost soil moisture sensing without losing predictive capability.This study calibrated the low-cost capacitance-based Spectrum Inc. SM100 soil moisture sensor using multiple least squares and machine learning models, with both laboratory and field data. The best calibration technique, field-based piece-wise linear regression (calibration r2 = 0.76, RMSE = 3.13 %, validation r2 = 0.67, RMSE = 4.57 %), was used to study the effect of sensor calibration on the performance of the FAO AquaCrop Open Source (AquaCrop-OS) model by calibrating its soil hydraulic parameters.This approach was tested during the wheat cropping season in 2018, in Kanpur (India), in the Indo-Gangetic plains, resulting in some best practices regarding sensor calibration being recommended. The soil moisture sensor was calibrated best in field conditions against a secondary standard sensor (UGT GmbH. SMT100) taken as a reference (r2 = 0.67, RMSE = 4.57 %), followed by laboratory calibration against gravimetric soil moisture using the dry-down (r2 = 0.66, RMSE = 5.26 %) and wet-up curves respectively (r2 = 0.62, RMSE = 6.29 %). Moreover, model overfitting with machine learning algorithms led to poor field validation performance. The soil moisture simulation of AquaCrop-OS improved significantly by incorporating raw reference sensor and calibrated low-cost sensor data. There were non-significant impacts on biomass simulation, but water productivity improved significantly. Notably, using raw low-cost sensor data to calibrate AquaCrop led to poorer performances than using the literature. Hence using literature values could save sensor costs without compromising model performance if sensor calibration was not possible. The results suggest the essentiality of calibrating low-cost soil moisture sensors for crop modeling calibration to improve crop water productivity.

Comparison of the AquaCrop and CERES‐Maize models for simulating maize phenology, grain yield, evapotranspiration and water productivity under different irrigation and nitrogen levels

AbstractThe CERES‐Maize and AquaCrop models were evaluated for their performance in simulating maize (Zea mays L.) phenology, evapotranspiration (ETc), grain yield and crop water productivity (CWP). Model input data for calibration, validation and simulations were obtained from field experiments conducted from 2011 to 2014 that imposed three different irrigation levels (full irrigation treatment [FIT], limited irrigation treatment [75% FIT] and rainfed control) and five nitrogen (N) management treatments. Both models performed well in simulating the maize phenological stages, with simulated values being within 1–4 days of measured values for both models. Both models simulated grain yield well during the calibration with a normalized root mean squared error (RMSEn) of 5% (0.6 t ha⁻1) for CERES‐Maize and 8% (0.80 t ha⁻1) for AquaCrop. The model error for ETc varied from −2 to +14% for CERES‐Maize and ranged from −3 to +11% for AquaCrop. High N levels resulted in higher accuracy in predicting ETc. The AquaCrop model performed better than CERES‐Maize in simulating irrigated crop production under different N levels under rainfed conditions. The performance of both models for rainfed systems was poorer than their performance in irrigated systems, indicating that both models need further improvements in simulating rainfed maize production systems.

Analysis of Crop Irrigation Water Requirements and Water Scarcity Footprint in the Beijing–Tianjin–Hebei Region Based on the GeoSim–AquaCrop Model

To reduce crop-related water consumption and enhance agricultural water resource efficiency in the Beijing–Tianjin–Hebei region, this study employed the AquaCrop model to simulate crop yield and irrigation water requirements and calculated the water scarcity footprint (WSF). The results were as follows: (1) The AquaCrop model exhibited strong applicability, with R2, RMSE (Root Mean Square Error), EF (Nash–Sutcliffe model efficiency coefficient) and d values of 0.9611, 6.6%, 0.91, and 0.98 (winter wheat), and 0.9571, 5.5%, 0.95, and 0.99 (summer maize) for canopy cover simulation. Similarly, aboveground biomass simulation yielded values of 0.9661, 0.8 t/ha, 0.93, and 0.98 (winter wheat), and 0.9087, 1.3 t/ha, 0.90, and 0.98 (summer maize). Winter wheat soil moisture content simulation showed an R2 of 0.9706, RMSE of 3.7 mm, EF of 0.93, and d of 0.98. (2) The AquaCrop model simulated the winter wheat and summer maize yields and irrigation water requirements for the years 2009, 2014, and 2019, validating the scalability and spatial visualization capabilities of GeoSim in extending AquaCrop simulations. (3) Integrating the water footprint and the water resources system, this study assessed the WSFs of winter wheat and summer maize. From 2009 to 2019, winter wheat production in the region increased by 25.08%, and summer maize production increased by 37.39%. The WSF of winter wheat decreased, whereas the WSF of summer maize increased. It is recommended to reduce crop cultivation areas in regions such as Daming County, Ningjin County, and Dingzhou City while further improving irrigation water efficiency, which would facilitate the sustainable utilization of water resources in the area.

Response of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China

The synchronicity of rain and heat in the summer of China’s monsoon region provides sufficient water and heat resources for maize growth. However, the intra-annual distribution of precipitation and the probability of extreme precipitation have been inevitably altered by the ongoing climate change, thus affecting the matching degree between precipitation and crop water requirements (MDPCWR). Evaluating the extent to which the MDPCWR will change in the future is of great importance for food security and the sustainable management of water resources. In this study, considering that different growth stages of crops have different sensitivities to water stress, the AquaCrop model was used to calculate the MDPCWR more accurately. In addition, a cumulative distribution function-transform (CDF-t) method was used to remove the bias of 11 global climate models (GCMs) under two typical emission scenarios (SSP2-4.5 and SSP5-8.5) from phase six of the Coupled Model Intercomparison Project (CMIP6). A comprehensive investigation was conducted on how maize growth, water consumption, and the MDPCWR will respond to future climate change with CO2 concentration enrichment in the Huang–Huai–Hai (3H) region in China by driving a well-tested AquaCrop model with the bias-corrected GCMs outputs. The results indicate the following: (1) The CDF-t method can effectively remove seasonal bias, and it also performs well in eliminating the bias of extreme climate events. (2) Under the SSP2-4.5 scenario, the average maximum temperature will increase by 1.31 °C and 2.44 °C in 2021–2050 and 2051–2080, respectively. The average annual precipitation will increase up to 96.8 mm/year, but it will mainly occur in the form of heavy rain. (3) The increased maize evapotranspiration rate does not compensate for the decreased crop water requirement (up to −32 mm/year), due to a shorter growth cycle. (4) The farmland cultivation layer is not able to hold a significant amount of precipitation, due to the increased frequency of heavy rains, resulting in increased irrigation water requirements for maize over the next two periods, with the maximum value of 12 mm/year. (5) Under different scenarios, the projected future MDPCWR will decrease by 9.3–11.6% due to changes in precipitation patterns and crop water requirements, indicating that it will be more difficult for precipitation to meet the water demand of maize growing in the 3H region. The results can provide comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization in the 3H region.

Land suitability and crop water requirements for irrigated sugar cane in the Kuraz irrigation scheme, lower Omo basin, Ethiopia

AbstractThe assessment of land suitability is the key to sustained agricultural output. This study evaluates the suitability of land for irrigated sugar cane cultivation and crop water requirements in the Kuraz Irrigation Scheme, Ethiopia. To accomplish this, the geographic information system–analytical hierarchy process (GIS–AHP) tool combination was employed to identify land suitability classes and aid in decision‐making using various factors, such as soil pH, texture, temperature, rainfall, land use–land cover and slope. The AquaCrop model simulated the current and future scenarios for biomass, canopy cover, yield, maximum evapotranspiration and evapotranspiration water productivity for sugar cane crops. AquaCrop was calibrated for irrigated sugar cane in the 2015–2016 crop season, and the results showed that most of the study area is highly suitable for sugar cane cultivation. Highly suitable areas cover 28%, moderately suitable areas 38%, marginally suitable areas 21%, and unsuitable areas 13%. The findings indicate the potential for expanding sugar cane cultivation and increasing yields and profits. However, it is crucial to consider the potential environmental degradation caused by water usage and soil degradation resulting from agricultural expansion. Furthermore, the social implications of expanding sugar cane cultivation should also be considered, including labour practices and the possible displacement of local communities.

Climate-smart irrigation strategy can mitigate agricultural water consumption while ensuring food security under a changing climate

North China Plain suffers from the world’s most severe water scarcity and groundwater depletion due to intensive irrigation for agricultural production. It is imperative to reduce irrigation water consumption while safeguarding crop production and food security. This study conducted a quantitative analysis with deficit irrigation strategies for winter wheat using a water-driven AquaCrop model. After model calibration and validation with field experimental data, we analyzed the irrigation water demand, crop yield, and water productivity (WP) of winter wheat under various deficit irrigation scenarios. A set of optimal irrigation schedules were proposed for different climate years, which significantly mitigated irrigation water usage while sustaining high yields and WPs. The results indicated that despite the irrigation water demand of winter wheat under the future climate scenario was slightly higher than that in the historical period, their crop water sensitive periods (reviving, jointing, and flowering) remained the same. Therefore, we recommended adopting the same deficit irrigation schedules for the historical and future periods. In wet years, adopting a 50% deficit irrigation strategy only reduced crop yields by less than 5% compared with full irrigation, but it saved 1000–1100 m3 of water per hectare and contributed a WP higher than 1.88 kg/m3. While in normal and dry years, an optimal 25% deficit irrigation could sustain over 96% of the maximum yield, meanwhile it could save 650–800 m3/ha of water and achieve almost the same WP as full irrigation. These climate-smart irrigation strategies adapting to diverse climatic conditions largely mitigate agricultural water consumption while maximizing crop productivity and water use efficiency, which are essential for achieving precision irrigation and sustainable water management under a changing climate.

Simulation of Winter Wheat Growth Dynamics and Optimization of Water and Nitrogen Application Systems Based on the Aquacrop Model

Winter wheat is the main grain crop in the Yellow River Basin, and optimizing water and nitrogen management can not only improve the yield, but also reduce water and fertilizer waste and environmental pollution. Using two years of winter wheat field trials, the Aquacrop model was calibrated and validated, and 36 different water and nitrogen application scenarios were simulated and analyzed. Among them, the field trials were divided into three N application levels, and three lower limits of water control. The results showed that: (1) the model has good applicability to simulate the growth process of winter wheat in the Yellow River Basin. (2) At a constant level of irrigation or nitrogen application, crop yield initially increased and subsequently decreased with the enhancement of either irrigation or nitrogen application. Finally, through response surface analysis of regression equations, the recommended irrigation volume was determined to be between 296 and 303 mm, and the fertilizer application rate was set between 229 and 261 kg/ha. At these levels, the PFP could achieve over 60% of its maximum. This research provides a theoretical basis for the water-saving, fertilizer-saving and high-yield water and fertilizer management system of winter wheat in the irrigated area of the Yellow River Basin.

Capacity of AquaCrop model in simulating performance variables and water use efficiency of spring rapeseed

A study was conducted to simulate the performance of rapeseed using the AquaCrop model at Khuzestan University of Agricultural Sciences and Natural Resources (Iran), over two crop years, 2021–2022 and 2022–2023. The results of the first year were used to calibrate the model, while the results of the second year were used to validate it. The research showed that the highest grain yield (1.572–1.415 tons/hectare) and water use efficiency (0.50–0.45 kg/m3) were obtained the first year from the control treatment, with a density of 110 plants m−2, and the Hayola 4815 cultivar, and the second year, with a density of 140 plants m−2. The normalized root mean square error (NRMSE) values were 1.82%, 15.36%, and 14.15% for grain yield, biomass, and water use efficiency, respectively, after the calibration phase and 15.4%, 19.99%, and 6.22%, after the validation phase. The model efficiency factor (EF) always resulted in values above 95%. According to the average relative error (RE), grain yield was overestimated both after calibration and validation steps, while biomass and water use efficiency were both underestimated. These results suggest that the simulations with AquaCrop model can be considered reliable for simulating spring rapeseed.

Using AquaCrop for Irrigation and water productivity assessment of Table grapes in arid region of Mexico

The aim of this work is to use the AquaCrop model for irrigation and water productivity assessment of Table grapes in arid region of Mexico during 2005 and 2006 cropping seasons. The irrigation efficiency was investigated by comparing the irrigation scheduling design used by the farmer to the AquaCrop model recommendations. Data analysis showed that the farmer irrigates almost every day, which results in the water content in the root zone always exceeding the soil moisture at field capacity (FC). This generates substantial losses of water through deep percolation. By using the AquaCrop model, the optimization of irrigation water scheduling in order to avoid both water stress and deep percolation was about 547 mm and 510 mm, which it is about half of what was applied by the farmer (1006 mm and 930 mm) during 2005 and 2006, respectively. This large difference, lost through deep percolation, reduces the water productivity (WP) by about 45%.

Analysis and Research on Production Effects of Full-Film Double Ridge-Furrow Mulching with Polyethylene Film and Biodegradable Film Based on AquaCrop

Water is an important factor limiting the development of arid rain-fed agriculture. Film mulching is an effective way to ensure yield in arid areas. However, whether biodegradable film can be used instead of polyethylene film for agricultural production in arid areas is a matter of contention. In this study, AquaCrop model simulation and field experiment were used to analyze the production effect of corn whole film double ridge furrow sowing technology covering polyethylene film (PM) and biodegradable film (BM) in Dingxi City from 2016 to 2020. The results showed that the AquaCrop simulation data have a high fitting with the field test data, and the model was suitable for simulating dry farming in Dingxi City. The best sowing time in Dingxi City is to select the average temperature to be stable at about 15 °C (around 15 April to 25 April each year), and the yield is the highest after sowing during this period. Although BM can achieve environmental protection and energy saving, it is weaker than PM in water storage and soil evaporation inhibition in arid areas. The average yield, aboveground biomass, water productivity, and harvest index of PM were 63.95%, 18.57%, 76.35%, and 38.22% higher than those of BM, respectively. In drought years, BM water stress on leaf expansion, induced stomatal closure, and premature senescence were 61%, 17%, and 9.5% higher than PM, respectively, and the stress time was 28.5 d, 5 d, and 26 d, respectively. The maximum canopy coverage and effective root zone water content were 24.5% and 30.49% lower, respectively. In the wet year, water stress under BM only had a certain effect on the leaf expansion of crops but had no effect on the induction of stomatal closure and premature senescence. The maximum canopy coverage and effective root zone water content were 13.56% and 31.35% lower, respectively. The above studies show that BM has a certain ability to store water and preserve moisture, but the parameters of PM are better than BM. Especially in dry years, the crop production efficiency of PM is more significant. It can be seen that in rain-fed agricultural areas with rainfall less than 500 mm, biodegradable film can not replace polyethylene film.

Yield prediction of wheat at different sowing dates and irrigation regimes using the AquaCrop model

Water efficiency models are playing an increasingly important role in helping agricultural activities adapt to climate change. AquaCrop is one of the models that can accurately correlate water-plant-climate parameters. In this study, the effects of irrigation strategies (I1; rainfed, I2; irrigation at Germination (G)+Tillering (T)+Heading (H) stages, I3; irrigation at G+H stages, I4; irrigation at G+T stages) and sowing dates (SD1; normal sowing date, SD2; late sowing date) on winter wheat yield and soil water conditions were investigated in semi-arid climate conditions. Biomass, grain yield, soil water content and crop canopy cover values observed in field conditions and simulated by AquaCrop. According to results SD1 did not have a negative effect on grain yield and biomass however SD2 would significantly reduce grain yield and biomass amount. Considering the biomass and grain yields in terms of irrigation, the highest yield was obtained in the irrigation water applied during the I2SD1 treatment. The yield reduction was 39% in rainfed treatments, 22% when irrigated in G+T periods, and 5% when irrigated in G+H stages. The model predicted 2-year grain yield and biomass values more accurately in SD1 than in SD2. The model predicted yield, biomass, soil moisture content and canopy cover values with an acceptable accuracy.

Calibration and validation of the AquaCrop model for production arrangements of forage cactus and grass in a semi-arid environment

Understanding the best strategies for growing forage plants is important in balancing livestock production, as is the improvement of management techniques aimed at increasing food production. Models that simulate plant growth are therefore important tools for agricultural planning. The aim of this study was to calibrate and validate the AquaCrop model for agricultural systems of the forage cactus, millet and sorghum under different production arrangements. Twelve production units were cultivated from 2019 to 2020, using different cactus clones, and millet and sorghum cultivars, with variations in crop configuration, ground cover, water regime and crop density. Data on the crops (phenology, biometry and biomass throughout the cycle, and productivity at harvest), climate, irrigation, soil and management were observed. In all, data from six production units were used for calibration and six for validation. The performance of the model was evaluated using statistical indices. AquaCrop resulted in admissible errors in predicting the productivity of the crops under evaluation, showing good accuracy with a coefficient of determination greater than 0.80 and performance classified as ‘very good’ (confidence coefficient > 0.80). The Orelha de Elefante Mexicana clone showed the highest mean water productivity (39 g m−2). It was concluded that the model can be used to simulate crop productivity under different cropping arrangements in agricultural environments in the semi-arid region of Brazil with the aim of meeting the forage deficit of livestock in the region.

Evaluation of the Impacts of Climate Change on Sunflower with Aquacrop Model

Climate change has become one of the most significant risk factors in agricultural production. Plant productivity declines caused by climate change pose a serious threat to food supply and security. Crop simulation models have been widely used in recent years for the assessment of the impacts of climate change on agricultural production. In Konya, there have been limited studies on the potential effects of climate change on sunflower production. Sunflower, the main crop of the most imported agricultural product group, in which the production amount is currently insufficient to cover domestic consumption demand, is strategically important for the Turkish economy. The goal of this study was to examine the effects of climate change on sunflower yield in Türkiye by using the Aquacrop model. The data of the field experiment carried out on the Ekllor sunflower cultivar for two years in Konya conditions were used as material. The daily projection dataset of three Global Climate Models (HadGEM2-ES, MPI-ESM-MR, GFDL-ESM2M) and two scenarios (RCP4.5 and RCP8.5) were used to analyze climate change impacts. The 1971-2000 period was considered as the reference period and the 2022-2098 period was selected as the future period. The results confirmed that the Aquacrop model was able to satisfactorily simulate yield with NRMSE 2.10 % for the rainfed condition and 10.55 % for the irrigated condition, a d-index of 0.97, and a modeling efficiency of 0.91. Aqaucrop climate change impacts simulation which was based on 3 global climate models covering with 2022 -2098 period simulations projected that sunflower yield would be decreased in a range of 21% to 44% for RCP4.5 and 18% to 50% for RCP8.5 scenarios under rainfed conditions. In contrast, the yield would be increased in a range of 11% to 23% for RCP4.5 and 10% to 33% for RCP8.5 scenarios under irrigated conditions. The findings point to the use of appropriate water management measures for future sunflower production as a means of adapting to climate change.

Evaluating the Yields of the Rainfed Potato Crop under Climate Change Scenarios Using the AquaCrop Model in the Peruvian Altiplano

Ensuring global food security and adapting to the challenges posed by climate change, particularly in rainfed agriculture, are paramount concerns. This research investigates the impacts of climate change on the yield of the potato crop variety Imilla Negra (Solanum tuberosum spp.) under the extreme climatic conditions of the Peruvian Altiplano. From the experimentation in six crop plots under a rainfed agricultural system, periodic crop growth parameter measurements were obtained from 2017 to 2018. The results showed a good performance of the AquaCrop model in the calibration and validation, successfully simulating crop growth and yield parameters. Climate projections showed precipitation decreases and temperature and evapotranspiration increases for the representative concentration pathway (RCP), RCP 4.5, and RCP 8.5 scenarios in 2023–2050. A comparison of crop yields between the base period (2006–2021) and the period 2023–2037 showed no significant changes, whereas a more considerable decrease was observed for the period 2038–2050. It is concluded that climate change generates moderate impacts on potato crop yields under the rainfed agricultural system in the Peruvian Altiplano due to the average reduction in precipitation.

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.