Crop Water Research Articles

Modern Drip Irrigation Technology on Tomato and Head Cabbage Production and Its Economic Feasibility at Misrak Silti District, Siltie Zone, Ethiopia

In Ethiopia various techniques were applied to improve on-farm irrigation water management under surface irrigation, especially on furrow irrigation system for last many years, however, it was very difficult to achieve threshold limit of water use efficiency, wisely use of scarce water resources in irrigated field, crop water productivity, precisely controlled application of irrigation water to plant roots, uniform water delivery to all plants, crop yield and its quality, regulate flow, deliver optimum crop water requirement, field water losses, groundwater withdrawal and save labor. It is great practical significance and series of measures using drip irrigation system to solve mentioned problems in irrigated field, because the irrigated agriculture is largest water-consuming sector in this area. This study was aimed to demonstrate drip irrigation technology, prove its economic feasibility and create skills of farmers and extension experts on implementation of the drip system on tomato (Galila 555) and head cabbage (Copen Hagen) productions within groundwater source area on Balo koriso main station in Misrak Silti woreda, Siltie zone, southern Ethiopia. The activity was done for four consecutive years (2019 to 2022 G. C) on fixed plot of 2500 square meter area in collaboration with Agricultural Research Institute and Techno serve project. During demonstration all the costs were considered to economic feasibility including initial investment cost. The study found that the use of drip irrigation saved 3690 m<sup>3</sup>/ha of water compared with use of furrow irrigation. This saved water may irrigate additional 2ha area of land by drip irrigation. This study also revealed that, a net income of 313066.04 ETB/ha in single irrigation season was obtained using modern drip irrigation technology. The farmers and local experts recognized noticeable saving water, fuel cost, irrigating time and labour, good crop performance from demonstrated drip irrigation system and shown interest to use the technology abundantly. Therefore, scaling up locally manageable drip irrigation system around the study area through comprehensive training of farmers and supportive staffs and allowing sufficient local market for drip kits and incentives or loan will be economically feasible and affordable to increase income and saves irrigation water.

Recent Methods for Evaluating Crop Water Stress Using AI Techniques: A Review.

This study systematically reviews the integration of artificial intelligence (AI) and remote sensing technologies to address the issue of crop water stress caused by rising global temperatures and climate change; in particular, it evaluates the effectiveness of various non-destructive remote sensing platforms (RGB, thermal imaging, and hyperspectral imaging) and AI techniques (machine learning, deep learning, ensemble methods, GAN, and XAI) in monitoring and predicting crop water stress. The analysis focuses on variability in precipitation due to climate change and explores how these technologies can be strategically combined under data-limited conditions to enhance agricultural productivity. Furthermore, this study is expected to contribute to improving sustainable agricultural practices and mitigating the negative impacts of climate change on crop yield and quality.

Impacts of landscape configurations on ecosystem services and their trade-offs across different landscape compositions

Landscape sustainability depends on the design of landscape patterns that can be guided by ecosystem services (ESs). Clarifying the impacts of landscape configurations on ESs and their trade-offs will facilitate landscape planning and management to achieve the best design without changing most landscape compositions. However, limited work has focused on these impacts, and whether these impacts are the same on different compositions remains unexplored. Thus, this study analyzes the impacts of landscape configurations on ESs and their trade-offs for different compositions. In this analysis, Gaussian mixture model was used to cluster the landscape by compositions, five important ESs (crop production, water yield, carbon storage, soil retention, and outdoor recreation) were quantified, and the constraint line was used to analyze the impact, taking the Xiangjiang River Basin as an example. Results showed that three clusters of landscape compositions, which are similar to the urban–rural–natural gradient, were identified. Furthermore, the impacts of landscape configurations on ESs and their trade-offs had largely the same direction in different compositions. However, change in landscape composition makes the high correlations among them weaker or shift to constraint impacts. Based on the results of the study, we can not only contribute to local landscape development but also argue three general approaches for landscape planning and management. First, considering the trade-offs and making a choice adapted to local conditions. Second, adjusting landscape configurations with virtually no changes of compositions. Third, external intervention based on rational landscape configurations.

Boosting resilience and yields in water-stressed sunflower through coordinated irrigation scheduling and silica gel applications

Sunflower (Helianthus annuus L.) is a globally significant oilseed crop, yet its yields may suffer due to water deficiency. A field experiment was conducted through two summer seasons (2021 and 2022) at the agricultural research station, Luxor, Egypt, to study the effects of irrigation regime, cultivar selection, and silica gel application on sunflower growth and yield parameters. The experiment utilized a split-split plot design with three irrigation levels based on field capacity (85 %, 70 %, 55 %), three sunflower cultivars (Sakha-53, Giza-102, and Giza-120), and two Silica gel treatments (0 and 300 kg/ha). Analysis of variance revealed significant effects of irrigation (P < 0.001) on all measured traits except 100-seed weight. Utilizing various cultivars also revealed substantial influence, affecting most traits consistently across the two seasons. Silica gel application positively impacted head diameter, 100-seed weight, oil content, and oil yield (P < 0.05). Furthermore, treatments combining moderate irrigation with silica gel application exhibited the highest water productivity, with values reaching 1.35 kg/m³ in the first season and 1.38 kg/m³ in the second season, indicating the synergistic effects of improved water management and silica gel on crop water use efficiency. Multivariate analysis of principal component analysis and clustering heatmap revealed that treatment combinations such as I2S1 (moderate irrigation with silica gel) optimized sunflower productivity, particularly for seed yield and oil content, reinforcing the importance of tailored agronomic practices for maximizing crop performance under varying environmental conditions. These findings underscore the significance of integrated agronomic approaches in enhancing sunflower productivity while promoting sustainable crop management practices.

Integrating ecosystem services trade-off, drivers and zoning into watershed water environment management in Nansihu Lake Basin, China

A series of ecosystem services (ESs) have a significant impact on water environment. How these ESs can be integrated into water environment management in the lens of coupled human and natural system (CHANS) is still lack exploration. This study proposes an integrating framework that connects the ESs with water environment management to facilitate the watershed sustainable development. We employ the InVEST model to evaluate typical ESs in the Nansihu Lake Basin (largest freshwater lake in Shandong, China), and analyze the spatiotemporal characteristics and trade-off of multiple ESs indicators. By revealing the interaction mechanism and key drivers of ESs and conducting ecological functional zoning, we investigate how ESs can facilitate water environment management in the Nansihu Lake Basin. The results indicated that: (1) The high-value areas of agricultural supply service are mainly distributed in the western plain area of the basin, while high-value areas of water provision, water purification and soil conservation services are in the mountainous and hilly areas in the eastern part of the basin. (2) There is a significant trade-off between crop supply service and water purification services. Crop and livestock supply services have a greater effect on water purification services (TN) than other services. (3) Crop area sown, total power of agricultural machinery and fertilizer application are the main drivers of water purification services (TN). (4) The basin can be divided into three ecological function zones, the agricultural production zone, the ecological regulation zone and the ecological redline zone. These findings imply that water environment management should be integrated with ESs by strengthening intersectoral collaboration, conducting zoning management in watershed planning and applying differentiated control measures in each zone. This includes optimizing the scale and structure of agriculture, and implementing circular economy and clean production practices. This study explored a comprehensive framework of integrating ESs into water environment management to facilitate the sustainable development of watershed.

Evolution of global water footprints of crop production in 1990–2019

Abstract Crop production has the largest water footprint (WF) of all economic sectors and ranks as a leading cause of water scarcity. Despite this, our understanding of historical changes in global WFs of crops remains limited. In this study, we analyse the recently published dataset on green and blue WFs of 175 individual crops in 1990–2019. We explore the main changes in unit WFs (expressed in m3t−1 yr−1) and WFs of production (m3 yr−1) and connect the observed changes to various physical and socio-economic drivers. We find that nearly 80% of crops reduced global average unit WFs (required less water per tonne) as crop yields improved and cultivation centred around more productive areas. However, the total WF of crop production increased by 30% as these productivity gains were insufficient to compensate for cropland expansion of mostly water-intensive crops. Close to 90% of the increase occurred between 2000–2019 likely driven by accelerated economic growth, globalisation, changing diets, and production of first-generation biofuels. Among crops, we observe the largest increases for oil palm fruit, soya beans, and maize as they became the main providers of crop-based nutrients, animal feed, and biofuels for the modern economy. Among regions, most of the increase occurred across the tropics, mainly in Indonesia, Brazil, and Nigeria. However, India, China, and the USA had the largest WFs of production over the study period. Humanity consumed 6.8 trillion m3 of water (87.1% green) to produce crops in 2019. This number is likely to increase in the future which may exacerbate already existing environmental and socio-economic issues. Thus, it is important to transition to more water-sustainable agrifood systems. Much potential still exists in increasing crop water productivity, shifting production to less water-scarce geographies, and rethinking our dietary and industrial consumption patterns.

Evaluation of crop water stress index of wheat by using machine learning models.

The Crop Water Stress Index (CWSI), a pivotal indicator derived from canopy temperature, plays a crucial role in irrigation scheduling for water conservation in agriculture. This study focuses on determining CWSI (by empirical method) for wheat crops in the semi-arid region of western Uttar Pradesh, India, subjected to varying irrigation treatments across two cropping seasons (2021-2022 and 2022-2023). The aim is to investigate further the potential of four machine learning (ML) models-support vector regression (SVR), random forest regression (RFR), artificial neural network (ANN), and multiple linear regression (MLR) to predict CWSI. The ML models were assessed based on determination coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) under diverse scenarios created from eight distinct input combinations of six variables: air temperature (Ta), canopy temperature (Tc), vapor pressure deficit (VPD), net solar radiation (Rn), wind speed (U), and soil moisture depletion (SD). SVR emerges as the top-performing model, showcasing superior results over ANN, RFR, and MLR. The most effective input combination for SVR includes Tc, Ta, VPD, Rn, and U (R2 = 0.997, MAE = 0.901%, RMSE = 2.223%). Meanwhile, both ANN and MLR achieve optimal results with input combinations involving Tc, Ta, VPD, Rn, U, and SD (R2 = 0.992, MAE = 2.031%, RMSE = 3.705%; R2 = 0.759, MAE = 13.95%, RMSE = 19.98%, respectively). For RFR, the ideal input combination comprises Tc, Ta, VPD, and U (R2 = 0.951, MAE = 5.023%, RMSE = 9.012%). The study highlights the considerable promise of ML models in predicting CWSI, proposing their future application in integration into an irrigation decision support system (IDSS) for crop stress mitigation and efficient water management in agriculture.

Determination of Critical Crop Water Stress Index of Tea under Drought Stress Based on the Intercellular CO2 Concentration

Climatic changes have caused seasonal drought to occur frequently in tea fields of low-mountain and hill regions over the past decades. This leads to huge losses in the quality and yields of famous tea, which restricts the economic development of the tea industry. It is crucial to implement suitable irrigation scheduling. The crop water stress index (CWSI) is the main index to assess the water status of the crop. When the crop suffers irreversible drought stress, its critical water status cannot be easily evaluated using the CWSI. The change from stomatal limitations (SLs) to non-stomatal limitations (NSLs) of photosynthesis is vital for accurately recognizing crop drought stress. Thus, the objective of this research is to determine the critical crop water stress index of tea based on intercellular CO2 concentration (Ci) dynamic responses to drought stress. During two sensitive periods of water stress (famous tea harvest season and summer drought season, which are from March to April and July to August, respectively), the dynamic changes in the CWSI in tea were calculated and analyzed based on the CWSI theoretical model. The upper and lower baselines were determined on a daily basis and during a certain period. A critical value of the CWSI represents irreversible drought damage. This was determined by the characteristic response of the Ci of tea leaves during extreme drought stress. The results showed the following: (1) during the famous tea harvest season and summer drought season, the daily variation in CWSI was similar. During a certain period, the former maintained a stable fluctuation, while the latter increased in fluctuation. (2) The Ci showed a trend of fluctuating downward to a low point and then upward during extreme drought stress. After reaching the low point, it quickly increased in the former and stabilized for a day in the latter. When the Ci reached the low point, the upper benchmark of this critical point was 13.9 μmol·mol−1, the lower benchmark was 3.4, and the CWSI was 0.27. This critical CWSI could be used as an irrigation threshold point to ensure normal production for tea fields.

Impact of lining of irrigation canals on groundwater recharge: A case study from Punjab province in Indus River basin of Pakistan

The Canal irrigation network of Pakistan's Indus River Basin (IRB) is one of the world's largest gravity-flow networks, with a significant portion located in Punjab province. The conveyance, delivery, and application of this water to the irrigated crops in the basin result in the loss of more than 50% of its water. This loss of water from the irrigation system is a major source of recharge for the large alluvial aquifer underlying the IRB. Canal lining is carried out as a measure to reduce seepage losses from the conveyance system of irrigation water. On the other hand, canal lining is considered a barrier to aquifer recharge. A study titled “Impact assessment of canal lining in Punjab,” funded by JICA, has been carried out in order to interrogate the comparative performance of lined and unlined canals and their impact on groundwater recharge. We measured seepage losses from 14 selected irrigation-lined canals in Punjab province under actual field conditions using ponding and/or inflow-outflow methods. Results have been compared with pre-lining seepage rates. The results indicate that canal lining has reduced seepage losses by 78%. The heavy pumping in fresh groundwater areas invites saline groundwater from adjacent saline zones. The canal flows at the head have reduced the recharge to groundwater by 9%. Nonetheless, saving from seepage losses by canal lining has increased available water for crops, but it has decreased groundwater recharge.

Penerapan IoT dengan Algoritma Fuzzy dan Mikrokontroler ESP32 dalam Monitoring Penyiraman

Smart farming has become a major focus in the development of modern agricultural technology. In this context, the Internet of Things (IoT) offers innovative solutions to increase productivity and efficiency in crop management. This research introduces an IoT-based plant watering monitoring system that uses a fuzzy algorithm and an ESP32 microcontroller. This system is designed to automatically regulate plant watering based on environmental conditions and plant needs. The ESP32 microcontroller acts as the brain of the system, collecting environmental data such as soil moisture, air temperature, and relative humidity. This data is analyzed using a fuzzy algorithm to determine plant watering needs in real-time. Based on the output of the fuzzy algorithm, the system automatically controls the water pump to water the plants according to the specified needs. The application of fuzzy algorithms allows the system to overcome uncertainty in plant watering decisions, especially in the face of complex variations in environmental conditions. With the adoption of IoT technology, farmers can monitor and control crop watering efficiently through web interfaces or mobile applications, even remotely. This research shows that the integration of IoT with fuzzy algorithms and ESP32 microcontrollers can be an effective solution in managing crop watering, increasing overall agricultural productivity, while reducing water and energy consumption.

Harvesting Rainwater and Utilizing for Supplemental Irrigation to Enhance Crop Productivity under Rainfed Conditions: A Case Study of North Eastern Ghats Zone of Odisha

Rainwater harvesting-cum-supplemental irrigation is an important strategy to enhance crop water productivity under rainfed conditions. This study was conducted to assess efficacy of farm pond in harvesting rainwater and utilizing for irrigation to okra-radish cropping sequence during 2018-19 and 2019-20 at Phulbani, Odisha. In this study, runoff harvested from a catchment area of 0.5 ha and stored in farm pond was quantified. Also, seepage and evaporation losses from the farm pond were determined along with computation of area that could be irrigated from the stored water. Three irrigation treatments were considered, namely, T1 – life-saving irrigation to okra and one protective irrigation of 5 cm to radish, T2 –life-saving irrigation to okra and two protective irrigations of 5 cm each to radish, T3 – control without any protective irrigation. Rainwater harvesting potential of the study areavariedfrom8.3% to 8.9% of rainfall. Evaporation and seepage losses from the pond varied from30.6% to27.4% and from18.21% to 17.4% during 2018 and 2019, respectively. About 26.35% and 24.8% of the harvested rainwater was utilized for providing life saving irrigations to 0.4 ha cropped area during Kharif and Rabi seasons in 2018 and 2019, respectively. Treatment T2 consisting of one life saving irrigation to okra and two irrigations to radish resulted in the highest okra equivalent yield (OEY) of 10,156 kg ha-1 and 10,228 kg ha-1during 2018 and 2019, respectively. Crop water productivity was found to be the highest, i.e.,0.63 and 0.632 kg m-3 during 2018 and 2019, respectively for treatment T2. Therefore, pond water can be used for providing life-saving irrigation to the vegetable crops, which resulted in higher returns due to increased crop yields. It will also be possible for the farmers to go for the second crop in Rabi season.

Innovation in Agriculture Advantages of a Remote Irrigation Monitoring and Control System

Globally, the irrigation of crops is the largest consumptive user of water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This Search discusses how Advantages of a remote irrigation monitoring and control system irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil–plant–atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

Projecting irrigation demand under IPCC climate change scenarios using WEAP modeling in the Rechna Doab, Pakistan

Pakistan is currently facing significant water scarcity issues, intensified by climate change. The main water source, the transboundary Indus River system, faces challenges such as water management, limited data availability, and inadequate management, leading to a gap between water demand and supply across various sectors. Agriculture, which consumes over 95 % of the country’s water resources, contributes nearly 25 % to the GDP, but is heavily dependent on irrigation due to limited rainfall. With rainfall meeting only 15 % of crop water requirements, groundwater plays a critical role, covering 40–60 % of irrigation needs. This study focuses on the Rechna Doab in the Indus Basin Irrigation System (IBIS) using the WEAP (Water Evaluation and Planning) model to assess the supply-demand gap under IPCC's climate change scenarios from Assessment Report Six (AR6). The main findings indicate: (1) Under SSP 8.5, unmet demand in the Upper Chenab Canal and other regions will increase by 33–47 % by mid-century; (2) demand site coverage will decline significantly, especially in Lower Gugera and Jhang branches; (3) groundwater dependency will increase substantially in response to the growing supply-demand gap. This work contributes to improving water management in Rechna Doab by providing a clear framework for adapting water resources to climate change using WEAP projections under various IPCC scenarios.

A coupled regional-scale numerical model for hydrological processes and interactions between groundwater and surface water in a controlled drainage district

Controlled drainage (CD) has emerged as an effective strategy to prevent field waterlogging and mitigate drought during crop growth by altering the hydrological process, while few studies have quantified its effect on groundwater-surface water interactions and groundwater recharge at a regional scale. In this study, a new model is developed for the coupled numerical simulation of water flow in ditches, soil, and underground aquifers under CD conditions. The domain is partitioned into horizontal sub-areas and two vertical layers, with ditches discretized into segments based on groundwater flow grids. The Saint-Venant equations, Richards equation, and MODFLOW are applied to describe water movement processes in ditches, soil, and underground aquifers, respectively. The proposed model is calibrated and validated using five years of observations of ditch water levels (DWL) and groundwater levels, demonstrating excellent agreement with observed data. Subsequently, water balance components of the shallow aquifer below 1 m depth (GW), 1 m of root layer soil profile (SW), and ditch water (DW) under seven scenarios with different control schemes during flood and dry seasons are analyzed to quantify variations in hydrological processes caused by CD. The results show that CD significantly impacts water within SW, GW, and DW, soil evaporation, infiltration, and net recharge from GW to SW (GtoS) by altering regional groundwater table depth (GWT) through the exchange between DW and GW (DtoG and GtoD). GWT and DW show moderate correlation with GtoD, while their correlation with DtoG varies significantly under different rainfall conditions. Smaller precipitation results that precipitation and GWT have a higher linear correlation with water balance components during the dry season compared to the flood season. The correlation values (r) between transpiration (T) and GWT range from −0.99 to 0.96 and −1 to −0.85 during the flood and dry seasons, respectively, indicating a pronounced influence of CD on crop growth. On average, a 1 m increase in DWL control scheme leads to a 0.51 m increase in DWL, and regional GWT decreases by 0.35 m and 0.24 m during flood and dry seasons, respectively. Furthermore, for every 1 m decrease in GWT, net GtoS increases by 36.1 mm and 28.1 mm, respectively, resulting in an increase in SW by 13.9 mm and 11.8 mm, respectively. Considering the varying main crop water stress and the impact of CD under different rainfall conditions, an appropriate CD scheme needs to be adjusted accordingly. These findings provide a quantitative reference for the effect of CD on regional hydrological process and serve as a typical case for similar areas aiming to implement CD strategies for waterlogging and drought control.

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.

Exploring the Effects of Irrigation Schedules on Evapotranspiration Partitioning and Crop Water Productivity of Winter Wheat (Triticum aestivum L.) in North China Plain Using RZWQM2

ABSTRACTA significant basis for winter wheat production in China is the North China Plain (NCP). However, winter wheat production is severely hampered by water shortages in this area. Transpiration co‐occurs with photosynthesis, affecting crop water productivity (CWP). The purpose of this experiment is to use Root Zone Water Quality Model (RZWQM2) to study actual transpiration (AT) and evaporation (AE) under different irrigation schedules and then determine its impact on grain yield and CWP. In the 2019–2022 winter wheat growing seasons, four experimental treatments were set up: no irrigation during growth period (I0), irrigation at jointing stage (I1), irrigation at jointing and anthesis stage (I2) and irrigation at jointing, anthesis and filling stage (I3), and the RZWQM2 model was calibrated and verified in this experiment. A higher yield (7840.90 kg/ha for an average of 3 years) and the highest CWP can be obtained in I2 treatment (increased by 12.72%, 5.98% and 4.28% for an average of 3 years, respectively, compared to the other three treatments). The model has a good simulation effect on soil water dynamic change and plant physiological performance of the four treatments; the model showed that irrigation increased the simulated AE and AT; however, reduced AE/actual evapotranspiration. For the whole growth period, AT in I3, I2, I1 and I0 was 351.70, 317.30, 271.50 and 223.70 mm, respectively. Especially in the late growth stage of winter wheat, the AT in I3 was 65.20 mm for an average of 3 years, which was significantly higher than I2, I1 and I0 by 31.60, 13.50 and 10.00 mm, respectively. Thus, I3 increased AT at the late growth stage of winter wheat and resulted in an increase in grain yield; however, it did not significantly increase CWP. This study demonstrated that irrigation at winter wheat jointing and anthesis stages can improve the CWP to achieve the goal of stable grain yield and water saving.

Long-term potato response to different irrigation scheduling methods using saline water in an arid environment

Crops’ water requirement is generally higher than the annual average precipitation in arid environments characterized by scarce freshwater resources. While using saline water for irrigation can help sustain agriculture in water-stressed regions, several challenges arises concerning productivity and soil salinization. However, adoption of efficient irrigation techniques such as drip irrigation, irrigation scheduling, and deficit irrigation can help optimize water productivity and mitigate salinity problems in irrigated agriculture. In southern Tunisia, potato is considered among the main cultivated horticultural crops due to its high economic value while it is considered as a crop sensitive to salinity. This crop (cv. Spunta) was the subject of long-term studies (2002–2020) conducted during the fall period in the arid region of Médenine. The crop response to full and deficit irrigation with saline water was assessed for several seasons under contrasting climatic conditions. Scheduling using the soil water balance (SWB) method consisted of the total and/or partial replacement of accumulated crop evapotranspiration (ETc), as derived from climatic data and crop coefficients. The impact of decreasing amounts of irrigation waters on crop yield and soil salinity with waters having a salinity ranging between 3 and 7 dS m−1 was evaluated. Results showed improvements in yield (30% to 37%) obtained with the SWB strategy under actual farming conditions, supporting the use of this strategy for irrigation. Appropriate scheduling also seems to be a key element in saving water (15%–22%) and in reducing risks of soil salinization. In the dry environment of southern Tunisia, optimum supply seems to correspond to a replacement of 100% to approximately 70%–80% of ETc. Applying such irrigation levels resulted in a lower salinity buildup in the root zone and higher crop water productivity. Natural salt leaching seems to be more effective under a more humid soil profile. Yield decreases and soil salinity increases almost linearly (r2 = 0.60) with decreasing irrigation water amounts. Future work should focus on the integration of management practices when using saline water. Investigating the relationship and interaction between irrigation amounts, cultivar, fertilizer supply, and salt leaching will help in resolving productivity and environmental issues.

Managing the Supply–Demand Mismatches and Potential Flows of Ecosystem Services in Jilin Province, China, from a Regional Integration Perspective

Regional integration strategically reorganizes spatially heterogeneous resources to maximize the overall benefits. Ecosystem services (ESs) are promising targets for regional integration due to their inherent heterogeneity and mobility, yet research in this area remains limited. This study quantifies crop production (CP), water yield (WY), carbon storage (CS), and habitat quality (HQ) for the years 2000, 2010, and 2020 using the InVEST model and identifies four ES bundles through a K-means cluster analysis. A conceptual ecosystem service flow (ESF) network at the service cluster scale is constructed based on county-level ESF data. The results reveal the following: (1) there is an upward trend in the ES budget for all services from 2000 to 2020, coupled with spatial mismatches between supply and demand; (2) deficit nodes for CP and CS services are concentrated in densely populated districts, while deficits in WY and HQ services are mainly in western Jilin Province; (3) Bundles I and II act as “sources” of ES, Bundle IV serves as a “sink”, and Bundle III is the only cluster with a CP surplus, balancing CP services across the province. In addition, this study provides ecological perspectives for understanding regional integration by suggesting differentiated integrated management for different ecosystem bundles.

Optimizing water-fertilizer integration with drip irrigation management to improve crop yield, water, and nitrogen use efficiency: A meta-analysis study

So far, many studies have examined the effects of drip irrigation and water-fertilizer integration on crop yield indices, nutrient use efficiency (NUE), and water use efficiency (WUE). However, they are carried out under different conditions. Due to the scattered research results and the lack of general conclusions from published results, it is necessary to use meta-analysis as a structured method to draw a conclusion. Therefore, a meta-analysis was conducted to quantitatively evaluate the effects of water–fertilizer integration with drip irrigation (WFIDI) on nitrogen use efficiency (NUE), water use efficiency (WUE), and crop yield to explore the most suitable application strategy of WFIDI, further discussion of its impact mechanism. A total of 352 studies in the Chinese region were included, of which 84 studies met the entry criteria for the meta-analysis, and 779 pairs of data were finally obtained. The meta-analysis results showed that WFIDI improved NUE (31.3 %), WUE (34.5 %) and crop yield (12.5 %) by changing soil physical and chemical properties (SOC increased by 36.4 %, TN increased by 9.3 %, and ETC decreased by 11.5 %). In order to screen the key influencing factors and determine the applicable conditions, PCA and win-win analysis were carried out. The results showed that WFIDI was suitable for food crops and fruit trees in medium-textured soils. It is recommended to use subsurface drip irrigation with a lower flow rate (≤1Lh−1) and nitrogen fertilizer (10–25 % lower than the traditional nitrogen application rate of farmers). This study provides inspiration for efficient and economical agricultural water management.

The novel triangular spectral indices for characterizing winter wheat drought

Agricultural drought threatens food security and agricultural sustainable development. There have been numerous spectral indices from remote sensing images developed for monitoring crop drought. However, most present spectral indices are focusing on crop growth and Land Surface Temperature (LST), and the crop canopy water content are in less consideration simultaneously. Additionally, the Normalized Difference Vegetation Index (NDVI) is used for characterizing crop growth in almost all spectral drought indices, with the spectral saturation problem of NDVI for closed crop canopy. When vegetation cover is high, NDVI values tend to saturate, which makes them insensitive to further changes in crop health. Therefore, the NDVI saturation phenomenon may lead to an underestimation of the extent of crop drought, as it is not effective in identifying subtle changes in crops under high-density vegetation conditions. Hence, we propose three novel triangular spectral indices for characterizing winter wheat drought using three features including LAI, Land Surface Water Index (LSWI) and LST. For validating the proposed spectral indices, we compared the agreement between these indices with measured Relative Soil Moisture (RSM) and Volumetric Water Content (VWC) of soil in agricultural meteorological station and present popular indices including Crop Water Stress Index (CWSI), Temperature-Vegetation Drought Index (TVDI), and Vegetation Health Index (VHI). The results revealed that our proposed indices including Euclidean distance Crop Health Index (ECHI), Difference Crop Health Index (DCHI) and Perpendicular Water Stress Index (PWSI) outperformed the popular CWSI, TVDI and VHI, with stronger correlations with measured RSM and VWC in agricultural meteorological station. Secondly, there are spatial consistencies for characterizing winter wheat drought between proposed ECHI, DCHI and PWSI with popular CWSI, TVDI and VHI. In addition, our proposed ECHI, DCHI and PWSI have achieved good performance of drought monitoring both in irrigated and rainfed croplands. All these results suggest that our proposed indices have great potential in crop drought monitoring.