Crop Water Demand Research Articles

Spatial and temporal patterns of agricultural drought in China during 1960–2020 characterized by use of the crop water deficit Abnormal Index

Agricultural drought intensifies crop water stress and limits photosynthetic productivity, thereby threatening food security and socio-economic development. However, current drought indices from model simulations or remote sensing have limitations with regard to fully considering the complex interactions between various climate factors and crop water demands. Furthermore, estimates from remote sensing-based drought indices are subject to significant uncertainties due to the coarse spatiotemporal resolution of the data sources, inherent systematic errors in methodologies, and the limited number of ground observations. This study aimed to bridge these gaps by employing the nationwide Agricultural Drought Monitoring Network covering over 2000 in-situ observations and the Crop Water Deficit Abnormal Index (CWDIa) to quantify the spatiotemporal characteristics of agricultural drought from 1960 to 2020. The duration and severity of drought in northern China were significantly greater than in the southern regions, especially in the North China Plain, and the northern Tibetan Plateau. The occurrence of severe drought in southwestern China and in the southern Tibetan Plateau during the spring and winter seasons was driven by persistent water deficits. Over the past 60 years, China has experienced an overall alleviation in the severity and duration of agricultural drought. However, the drought situation has continued to worsen in certain regions, such as in southwestern China and the southern North China Plain. Multiscale decomposition of the ensemble empirical mode has further strengthened this finding (i.e., the drying trend in southwestern China since 1990). As seen in authoritative drought statistics, the integration of CWDIa and dense observations in the drought monitoring framework have demonstrated robust efficacy in capturing over 80 % of the actual drought-covered and drought-affected area. These findings highlight the importance of incorporating climate and crop water demand in agricultural drought monitoring to provide reliable and long-term spatiotemporal estimates of drought characteristics based on a nationwide observation network.

Crop Water Requirement Prediction Method Based on EEMD-Attention-LSTM Model

Crop water demand prediction is an important part of Precision agriculture. Due to the nonlinear relationship between input variables (weather data, soil moisture, and crop type) and output variables (crop water demand), it is difficult to accurately predict crop water demand. This article proposes a method for predicting crop water demand based on the EEMD Attention LSTM model. The model combines the ensemble empirical mode decomposition (EEMD), attention mechanism (Attention), and Long short-term memory (LSTM) neural networks to capture the changes in different scales of input variables. It selectively focuses on the relevant parts of the input sequence when making predictions. The performance of the EEMD attention LSTM model was evaluated on the historical crop water demand data set and compared with other prediction models such as RNN, LSTM, and EMD-LSTM. It shows that the model is superior to other methods in terms of Mean squared error and mean absolute error. When the relationship between input variables and output variables is nonlinear, the EEMD-attention-LSTM model is a powerful tool for crop water demand prediction.

SAFER-ET based assessment of irrigation patterns and impacts on groundwater use in the central Punjab, Pakistan

The excessive pumping of groundwater during the dry season in Pakistan is identified as a significant concern for groundwater sustainability. Farmers encounter difficulties in efficiently managing irrigation, and adopting the outputs of Irrigation Advisory System (IAS) to improve irrigation practices. Upgrading the existing IAS can help to identify the regions with over-irrigation issues and optimize outreach efforts. To solve these issues, we define the new concept of integrating Gravity Recovery and Climate Experiment (GRACE) total water storage anomalies (TWSA) and Landsat 8 imageries, which can enhance the IAS efficiency by accurately identifying regions facing groundwater depletion by estimating actual crop water consumption through application of the Simple Algorithm For Evapotranspiration Retrieving (SAFER) on Landsat 8 data and Penman-Monteith (PM) method was used to calculate crop water demand using meteorological forcing data from Global Land Data Assimilation System (GLDAS) over the five selected districts of Punjab during the dry seasons of year 2013–2020. Comparing actual water consumption (SAFER evapotranspiration (ET)) with in-situ groundwater depth, strong correlations were found between them that are supporting the use of Landsat 8 data for irrigation monitoring. Further, compared SAFER ET was with PM ET to calculate the percentage of over/under irrigated regions, and the results revealed that most of the selected districts were over-irrigated, indicating the potential for excessive irrigation water savings. Our results show that the integration of GRACE TWSA and Landsat 8 data enhance the efficiency of operational IAS and can lead to potential savings of 81% (equivalent to 155 million m3) of groundwater during the dry season in the Central Punjab, Pakistan. Satellite data integration globally enhances IAS implementation, aiding regions with unsustainable dry season irrigation. This empowers farmers to optimize agriculture through precise IAS outputs.

Estimating uptake and internal transport dynamics of irrigation water in apple trees using deuterium-enriched water

With future climatic scenarios foreseeing increased crop water demands and reduced irrigation water availability, a deeper knowledge on the dynamics of water uptake and translocation inside plants is needed. Little is known about the time interval existing between the irrigation water supply and the presence of irrigation water inside trees, and whether the dripper localization can affect water uptake and translocation dynamics. Another research gap concerns the redistribution of irrigation water in the canopy following irrigation localized to one side of the tree.A field and a pot experiment were designed to gain more insight into this context. In the field experiment, we tested the effect of different drip irrigation layouts on the extent and velocity of water uptake by apple trees. Trees were irrigated using deuterium-enriched water using one, two, or four drippers per tree. Samples were collected from different heights in the canopy at regular intervals following the irrigation event. In the pot experiment, the soil was saturated with labelled water and samples were collected at different time intervals and heights along the tree stem. Labelled water was detected in the lowest stem section of potted trees after 1 h from irrigation. In field-grown trees, labelled water appeared in the shoots after 4 h and 6 h in the bottom and top part of the canopy, respectively. By increasing the number of drippers per tree, the fraction of irrigation water in the shoots increased accordingly. However, uptake and transport velocity were unaffected by the number of drippers, averaging 0.60–0.65 m h-1. In trees that were irrigated from one side only, irrigation water could be found on the opposite side in the top part of the canopy. Our results suggest that the localization and amount of irrigation water can significantly influence root water uptake in apple trees.

Vertical agrivoltaics and its potential for electricity production and agricultural water demand: A case study in the area of Chanco, Chile

This paper presents a cross-sector analysis of a 100 kWp vertical agrivoltaic (AV) case study in Chanco, Maule, Chile. Maule is an agricultural region facing recurring droughts, which put pressure on irrigated lands. The study investigates the potential of vertical AV in two ways: comparing the energy yield prediction of the photovoltaic component with a typical north-tilted PV plant and comparing the water demand of a reference crop in vertical AV with open field conditions. A PVLib and PVFactors python tools were used to evaluate energy production, while spatial evapotranspiration prediction incorporates wind speed and solar irradiation heterogeneities. Results for the climatic year 2021 indicate that a north-tilted power plant produced more energy than a bi-facial vertical AV plant, but the latter represents a significantly less impact on agricultural activities. The analyzed vertical AV presents a lower impact to the grid due to the two peaks in daily power production that spread the generation over the day and does not contribute to the overproduction in the midday that is currently being curtailed when high solar irradiance is present in Chile. Water savings of up to 1410 m3/ha were found in the study with the vertical AV installation mainly due to the reduced irradiation combined with windbreak effects.

Warming temperatures exacerbate groundwater depletion rates in India.

Climate change will likely increase crop water demand, and farmers may adapt by applying more irrigation. Understanding the extent to which this is occurring is of particular importance in India, a global groundwater depletion hotspot, where increased withdrawals may further jeopardize groundwater resources. Using historical data on groundwater levels, climate, and crop water stress, we find that farmers have adapted to warming temperatures by intensifying groundwater withdrawals, substantially accelerating groundwater depletion rates in India. When considering increased withdrawals due to warming, we project that the rates of net groundwater loss for 2041-2080 could be three times current depletion rates, even after considering projected increases in precipitation and possible decreases in irrigation use as groundwater tables fall. These results reveal a previously unquantified cost of adapting to warming temperatures that will likely further threaten India's food and water security over the coming decades.

Digested slurry analysis for utilization based on irrigated crop water demand

To rationally utilize digested slurry, and thereby promote the integrated development of planting and breeding in breeding industry, the water-fertilizer requirement analysis of typical winter wheat-summer corn rotation tillage model in Zhengzhou was completed based on the water/nutrient requirement of crops. The results showed that the water demand and effective rainfall of winter wheat during the whole growth period were 492 and 190 mm, and the application amount of digested slurry was 3090 m³. The N, P, and K provided were 3600, 197, and 1310 kg, respectively, which exceeded the normal nutrient requirement of wheat. During the whole growth period of summer corn, the water demand was 354 mm, the effective rainfall was 290 mm, and the application amount of digested slurry was 763 m³. The amount of N, P, and K provided was 890, 48.6, and 324 kg, respectively. The amount of N and K exceeded the normal nutrient demand of corn, while the P was insufficient. Therefore, digested slurry can provide full nutrient requirements in winter wheat planting season in Zhengzhou, and P fertilizer should be added when applying digested slurry in corn planting season. If long-term application, excessive nutrient loss may cause non-point source pollution.

Irrigation benefits outweigh costs in more US croplands by mid-century

Irrigation can increase crop yields and could be a key climate adaptation strategy. However, future water availability is uncertain. Here we explore the economic costs and benefits of existing and expanded irrigation of maize and soybean throughout the United States. We examine both middle and end of the 21st-century conditions under future climates that span the range of projections. By mid-century we find an expansion in the area where the benefits of irrigation outweigh groundwater pumping and equipment ownership costs. Increased crop water demands limit the region where maize could be sustainably irrigated, but sustainably irrigated soybean is likely feasible throughout regions of the midwestern and southeastern United States. Shifting incentives for installing and maintaining irrigation equipment could place additional challenges on resource availability. It will be important for decision makers to understand and account for local water demand and availability when developing policies guiding irrigation installation and use.

Major challenges in widespread adaptation of aerobic rice system and potential opportunities for future sustainability

Climate change and diminishing resources are the major challenges in sustainable rice production. The aerobic rice system can be a transformational approach to replace conventional rice in the face of climate change and scarcity of resources due to its high input-use efficiency. However, it could not be adapted widely yet due to several challenges like higher weed infestation and low nitrogen (N) use efficiencies. This transformed system can only be adapted at a wider scale through investigation of associated constraints and risks and provision of potential adjustive measures. Aerobic rice crop failure can be reduced if the availability of suitable and stress-resistant cultivars with early improved vigour, and short-duration characteristics are ensured for maintaining the reproductive capability under resource stressed environment. This transformed rice system exhibits limited response to applied N because of increased losses due to higher volatilization and rapid coupled nitrification-denitrification processes under alternate wetting and drying systems. Shifting from the flooded to aerobic rice system bringing changes in crop water demand, organic matter (OM) turnover, N dynamics, weed flora, and greenhouse gases (GHGs) emissions. Along with N-losses, continuous monocropping, higher weed infestation, weedy rice emergence, nitrous oxide (N2O) emissions, nutrient disorders, higher prevalence of pathogens and diseases, increased panicle sterility, and an increase in soilborne pathogens lodging are other major constraints negatively impacting the widespread adaptation of aerobic rice. This article reviews the climate change status, its associated impacts on the sustainability of traditional rice systems, and the rationale for transforming from traditional to aerobic rice systems. Furthermore, it describes the major challenges associated with the aerobic rice system, thereby presenting management strategies ensuring its wider adaptation for sustainable rice production in the face of projected climate change and scarcity of resources.

Balancing crop water requirements through supplemental irrigation under rainfed agriculture in a semi-arid environment

Rainfed farming is a dominant agricultural system in Tigray, Ethiopia. However, rainfall is characterized by short duration, intense and erratic subjected to late-onset and early cessation, suggesting a pressing need for Supplemental Irrigation (SI) to fill the crop water demand. Understanding the effects of SI during rainfall late-onset, early cessation, or both, along with their underlying causes, is a critical knowledge gap globally. Although wheat is one of the principal food crops in Tigray, it is subjected to moisture stress during critical growth stages, limiting its potential productivity. Studies specifically related to impacts due to the late-onset of rainfall on wheat are non-existent. Here, we investigated the agrometeorological characteristics of rainfall variability, onset, cessation, and length of the growing season to evaluate the use of SI for balancing the moisture stress in rainfed farming. Meanwhile, using an on-farm experiment, we also evaluated double-season (2017 and 2018) SI application during late-onset (Pre), early cessation (Post), and its combined effects (Pre + Post) on yield and water productivity (WP) of wheat. Yield and WP were significantly (P < 0.05) affected by SI application with higher grain yield (3298 kg/ha) and WP (0.538 kg/m3) obtained from applying Pre + Post. Applying Pre + Post has increased grain yield, biomass, and WP of wheat by 45.6, 27.7, and 21.5% over Rain-fed farming, respectively. Thus, balancing crop water requirements using SI during inadequate rainfall distribution is key for improving WP and wheat production in semi-arid environments. Particularly, the application of SI both during the late-onset and early cessation of rainfall is suggested for greater wheat productivity in semi-arid regions.

Adaptation to seasonal drought in irrigation districts of south China: A copula-based fuzzy-flexible stochastic multi-objective approach for precise irrigation planning

The occurrence of seasonal drought has led to periodic water shortages in irrigation districts of south China, posing significant agricultural challenges on water and food security. Consequently, there is a pressing need for high-efficient water resources management from a sustainability perspective. To address this issue, a copula-based fuzzy-flexible stochastic multi-objective programming (CFSMP) model was proposed to facilitate irrigation planning under the combined influence of seasonal dry and wet conditions. The model was aimed to simultaneously achieve maximum economic benefit, water productivity and green water use efficiency, considering the economic, social, and environmental spheres of the irrigation district. The CFSMP model represented an innovative solution that was capable of (1) precisely describing the response of crop yield to water supply at ten-day time scale, which can be matched with practical irrigation demand; (2) characterizing joint distributions of seasonal dry and wet conditions of precipitation during different crop growth periods based on the copula function; (3) reconciling conflicts on objectives among economic, social, and environmental spheres of the irrigation district in complex and uncertain environments. The proposed model was applied to the Dongfeng Reservoir Irrigation District in south China. The downscaled ten-day Jensen water production functions of main grain and cash crops were fitted to recognize precise distributions of crop water demand and water sensitivity index. The Frank copula, with the smallest value of squared Euclidean distribution, was selected to describe the joint distribution of seasonal precipitation and gain insights into actual seasonal water availability and water scarcity. The results showed that the proposed model produced more water-saving and yield-promoting irrigation planning policies than the current quota. The average water allocation of rice, wheat, maize, rape, and citrus decreased by 352.6 mm, 10.6 mm, 67.7 mm, 7.44 mm, and 54.6 mm, respectively. Except for a yield reduction of 8.8% for rape, crop yields of rice, wheat, maize, and citrus were improved by 33.3%, 45.4%, 29.9%, and 12.3%, respectively. The coordination degree of the model reached 0.827, demonstrating excellent performance on generating coordinated and robust solutions. Therefore, the CSFMP model has the potential to alleviate seasonal drought in south China and can be applied in similar regions with comparable resources crisis.

The FC Algorithm to Estimate the Manning’s Roughness Coefficients of Irrigation Canals

Freshwater scarcity has driven the integration of technological advancements and automation systems in agriculture in order to attempt to improve water-use efficiency. For irrigation canals, water-use efficiency is, in great measure, limited by the performance of management systems responsible for controlling the flow and delivering water to the farmers. Recent studies show a significant sensitivity of the results obtained from irrigation canal control algorithms with respect to the Manning’s roughness coefficient value, thus, highlighting the importance of its correct estimation to ensure an accurate and efficient water delivery service. This is the reason why the friction coefficient algorithm was developed, to monitor the real behaviour of any irrigation canal by calculating the Manning’s roughness coefficient constantly. The friction coefficient algorithm was conceived as a powerful offline tool that is integrated in a control diagram of any irrigation canal, concretely in an optimization control algorithm, which can reconfigure canal gates according to the current crop water demand and the real Manning’s roughness coefficient values. The friction coefficient algorithm has been applied in several irrigation canals and different scenarios, with accurate results obtaining an average Manning coefficient deviation among 2 × 10−4 and 4.5 × 10−4.

Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning

The real-time nondestructive monitoring of plant water content can enable operators to understand the water demands of crops in a timely manner and provide a reliable basis for precise irrigation. In this study, a method for rapid estimation of water content in Aquilaria sinensis using multispectral imaging was proposed. First, image registration and segmentation were performed using the Fourier–Mellin transform (FFT) and the fuzzy local information c-means clustering algorithm (FLICM). Second, the spectral features (SFs), texture features (TFs), and comprehensive features (CFs) of the image were extracted. Third, using the eigenvectors of the SFs, TFs, and CFs as input, a random forest regression model for estimating the water content of A. sinensis was constructed, respectively. Finally, the monarch butterfly optimization (MBO), Harris hawks optimization (HHO), and sparrow search algorithm (SSA) were used to optimize all models to determine the best estimation model. The results showed that: (1) 60%–80% soil water content is the most suitable for A. sinensis growth. Compared with waterlogging, drought inhibited A. sinensis growth more significantly. (2) FMT + FLICM could achieve rapid segmentation of discrete A. sinensis multispectral images on the basis of guaranteed accuracy. (3) The prediction effect of TFs was basically the same as that of SFs, and the prediction effect of CFs was higher than that of SFs and TFs, but this difference would decrease with the optimization of the RFR model. (4) Among all models, SSA-RFR_CFs had the highest accuracy, with an R2 of 0.8282. These results confirmed the feasibility and accuracy of applying multispectral imaging technology to estimate the water content of A. sinensis and provide a reference for the protection and cultivation of endangered precious tree species.

Impacts of climate change on future crop water demand in an agricultural watershed in Mayurbhanj district of Odisha, India

Impacts of climate change on future crop water demand in an agricultural watershed in Mayurbhanj district of Odisha, India

A Full-Scale Optimization of a Crop Spatial Planting Structure and its Associated Effects

Driven by the concept of agricultural sustainable development, crop planting structure optimization (CPSO) has become an effective measure to reduce regional crop water demand, ensure food security, and protect the environment. However, traditional optimization of crop planting structures often ignores the impact on regional food supply–demand relations and interprovincial food trading. Therefore, using a system analysis concept and taking virtual water output as the connecting point, this study proposes a theoretical CPSO framework based on a multi-aspect and full-scale evaluation index system. To this end, a water footprint (WF) simulation module denoted as soil and water assessment tool–water footprint (SWAT-WF) is constructed to simulate the amount and components of regional crop WFs. A multi-objective spatial CPSO model with the objectives of maximizing the regional economic water productivity (EWP), minimizing the blue water dependency (BWFrate), and minimizing the grey water footprint (GWFgrey) is established to achieve an optimal planting layout. Considering various benefits, a full-scale evaluation index system based on region, province, and country scales is constructed. Through an entropy weight technique for order preference by similarity to an ideal solution (TOPSIS) comprehensive evaluation model, the optimal plan is selected from a variety of CPSO plans. The proposed framework is then verified through a case study of the upper–middle reaches of the Heihe River Basin in Gansu province, China. By combining the theory of virtual water trading with system analysis, the optimal planting structure is found. While sacrificing reasonable regional economic benefits, the optimization of the planting structure significantly improves the regional water resource benefits and ecological benefits at different scales.

An Integrated Approach to Evaluating Crop Water Requirements and Irrigation Schedule for Optimizing Furrow Irrigation Design Parameters in Kurnool District, India

In Kurnool district, due to the hot, arid climatic conditions, proper study of crop evapotranspiration and its effect on crop water demand for various crops is an extremely important issue. More focus will be given to the design and development of surface irrigation systems based mostly on furrow irrigation, as the rainfall pattern for this district was irregular in 2005. The crop water requirement and furrow irrigation design parameters for optimising the beneficial utilisation of available water resources and field performances are studied in this research. Some major crops, such as cotton, sugarcane, sorghum, maize, and sunflower, are considered when adopting a suitable furrow irrigation system for soil conditions of the types black clay, red loamy, and medium loam. The reference crop evapotranspiration was estimated for 2005 based on the Penman–Monteith equation as per FAO guidelines by the CROPWAT 8.0 model. Crop Water Requirement (CWR), Net Irrigation Requirement (NIR), and Gross Irrigation Requirement (GIR) were determined for available climatic and soil conditions. Soil surface characteristics were studied using AQUACROP 6.1 and compared with CROPWAT 8.0 for model accuracy. Various conservative and non-conservative crop characteristics were studied under limited set conditions, and correlation equations were developed between different parameters. Moreover, different furrow irrigation design parameters were considered by FURDEV (a module of the SURDEV model) for three modes of operation (i.e., fixed flow, cutback flow, and tailwater reuse method) under modified SCS (Soil Conservation Service) families of soil infiltration characteristics. NIR values were 200 mm, 1423.2 mm, 220 mm, 150 mm, and 150 mm for cotton, sugarcane, maize, sorghum, and sunflower, respectively. A maximum significant dry yield was observed for maize (13.586 tonnes/ha). Additionally, the maximum application efficiency and storage efficiency were obtained at 95.5% for sorghum and 99.3% for sunflower, which would be a beneficial outcome of this research. In general, the results of this research might be very effective for the irrigation authority of Kurnool District to plan suitable approaches for designing and developing proper water management systems.

Measuring the crop water demand and satisfied degree using remote sensing data and machine learning method in monsoon climatic region, India.

Supply of water is one of the most significant determinants of regional crop production and human food security. To promote sustainable management of agricultural water, the crop water requirement assessment (CropWRA) model was introduced as a tool for the assessment of satisfied degree of crop water requirements (CWR). Crop combination, water availability for agricultural production, water accessibility, and other indices were calculated considering the DEM, hydrological and climatic data, and crop properties for measuring the agricultural water requirement and satisfied degree in Bansloi River basin using the CropWRA model. Advanced machine learning model random forest was used to calculate the soil moisture considering the atmospheric variable, Landsat indices, and energy balance components for calculating the crop water satisfied degree and water requirement. The average crop water demand is 1.92m, and it ranges from 1.58 to 2.26m. The demand of crop water is more in the western part of the basin than the eastern part. The CropWSD (crop water satisfied degree) ranges from 17 to 116% due to variation in topography, river system, crop combination, land use, water uses, etc. The average crop water satisfied degree is 59%. About 71% of the total area is under 40% to 60% CropWSD level. CropWRA model can be applied for the sustainable water resource management, irrigation infrastructure development, and use of other modern technologies.

Land Use Misclassification Results in Water Use, Economic Value, and GHG Emission Discrepancies in California’s High-Intensity Agriculture Region

California’s San Joaquin Valley is both drought-prone and water-scarce but relies on high-intensity agriculture as its primary economy. Climate change adaptation strategies for high-intensity agriculture require reliable and highly resolved land use classification data to accurately account for changes in crop water demand, greenhouse gas (GHG) emissions, and farmgate revenue. Understanding direct and indirect economic impacts from potential changes to high-intensity agriculture to reduce groundwater overdrafts, such as reductions in the cultivated area or switching to less water-intensive crops, is unachievable if land use data are too coarse and inconsistent or misclassified. This study quantified the revenue, crop water requirement, and GHG emission discrepancies resulting from land use misclassification in the United States’ most complex agricultural region, California’s San Joaquin Valley. By comparing three commonly used land use classification datasets—CropScape, Land IQ, and Kern County Agriculture—this study found that CropScape led to considerable revenue and crop water requirement discrepancies compared to other sources. Crop misclassification across all datasets resulted in an underestimation of GHG emissions. The total revenue discrepancies of misclassified crops by area for the 2016 dataset comparisons result in underestimations by CropScape of around USD 3 billion and overestimation by LIQ and Kern Ag of USD 72 million. Reducing crop misclassification discrepancies is essential for crafting climate resilience strategies, especially for California, which generates USD 50 billion in annual agricultural revenue, faces increasing water scarcity, and aims to reach carbon neutrality by 2045. Additional investments are needed to produce spatial land use data that are highly resolved and locally validated, especially in high-intensity agriculture regions dominated by specialty crops with unique characteristics not well suited to national mapping efforts.

The use of alternative irrigation and cropping systems in forage production may alleviate the water scarcity in semi-arid regions.

Strategies to enhance the efficient use of irrigation water require a major shift in irrigation and cropping systems. It was hypothesized that (i) replacing water-demanding crops such as corn silage with more drought-tolerant forages species, (ii) adoption of intercropping instead of monoculture, and (iii) use of alternative irrigation methods, may alleviate the water shortage in semi-arid regions, while producing high-quality forage. Adoption of drip irrigation (DRIP) and alternate furrow irrigation (AFI) reduced water consumption by 43% and 20%, respectively. Additionally, DRIP produced 11% more biomass than the conventional furrow irrigation. The intercropped ratio of 50% sorghum and 50% amaranth under DRIP maximized forage production and improved irrigation water-use efficiency (IWUE). Principal component analysis indicated that the DRIP increased the dry matter yield and IWUE, whereas the AFI improved the forage quality. The intercropped ratio of 75% sorghum and 25% amaranth demonstrated the highest yield stability and was considered superior cropping system regardless of the irrigation strategies. DRIP and AFI strategies were effective in reducing water consumption, with DRIP being the most water-efficient method. Intercropping sorghum and amaranth at a ratio of 50:50 under DRIP resulted in the highest forage yield and IWUE. While sole amaranth had the highest forage quality, intercropping sorghum and amaranth increased dry matter production with better forage quality than sorghum monoculture. Overall, the combination of DRIP and intercropping sorghum and amaranth at a ratio of 50:50 considered as a suitable strategy for improving forage yield and quality, as well as IWUE. © 2023 Society of Chemical Industry.

Solutions to agricultural green water scarcity under climate change.

Rain-fed agricultural systems, which solely depend on green water (i.e. soil moisture from rainfall), sustain ∼60% of global food production and are particularly vulnerable to vagaries in temperature and precipitation patterns, which are intensifying due to climate change. Here, using projections of crop water demand and green water availability under warming scenarios, we assess global agricultural green water scarcity-defined when the rainfall regime is unable to meet crop water requirements. With present-day climate conditions, food production for 890 million people is lost because of green water scarcity. Under 1.5°C and 3°C warming-the global warming projected from the current climate targets and business as usual policies-green water scarcity will affect global crop production for 1.23 and 1.45 billion people, respectively. If adaptation strategies were to be adopted to retain more green water in the soil and reduce evaporation, we find that food production loss from green water scarcity would decrease to 780 million people. Our results show that appropriate green water management strategies have the potential to adapt agriculture to green water scarcity and promote global food security.