Agricultural Water Productivity Research Articles

Trend and change-point analyses of meteorological variables using Mann–Kendall family tests and innovative trend assessment techniques in New Bhupania command (India)

Abstract Climate change (CC) significantly influences agricultural water productivity, necessitating increased irrigation. Therefore, the present study was undertaken to assess the trend and change-point analyses of weather variables such as temperature (T), rainfall (R), and reference evapotranspiration (ET0) using 31-year long-term data for semi-arid climate. The analysis was carried out employing Mann–Kendall (MK), Modified Mann–Kendall (MMK), Innovative Trend Analysis (ITA), and Innovative Polygon Trend Analysis (IPTA) methods. Homogeneity tests, including Pettitt's test, Standard Normal Homogeneity Test (SNHT) , Buishand range test, and Von Neumann Ratio Test (VNRT), were employed to detect change points (CPs) in the time series data. The results indicated that, for maximum temperature, MK and MMK revealed a positive trend for September and July, respectively, while minimum temperatures indicated increasing trends in August and September. Rainfall exhibited an increasing trend during the Zaid season (April–May). ET0 exhibited a negative trend in January. ITA and IPTA displayed a mixture of positive and negative trends across months and seasons. The change-point analysis revealed that for Tmax, the CP occurred in 1998 for time-series data for the month of April. Likewise, for Tmin, the change points for April and August time series were found in 1997.

Accounting for water quality—A framework for agricultural water use

Driven by increasing water demand, scarcity concerns, and climate change impacts, numerous countries prioritize solutions for enhanced water use efficiency. However, these solutions often focus primarily on managing water quantities to improve water productivity in agriculture, urban, and industrial sectors. Effective and sustainable water use, however, requires monitoring and management of both water quantity and quality. Traditionally, water quantity and water quality have been managed separately, often by different government agencies with different missions and limited interaction. Ensuring sufficient water quantity for agriculture and food production often takes precedence over managing water quality. Water accounting, as a tool for allocating and managing water quantity is now widely accepted and numerous examples of successful implementation exist worldwide. However, the concept of incorporating water quality into water accounting has not yet been widely promoted. Measuring both quantity and quality in the same water bodies is a fundamental principle of assessment of impacts on water quality through the determination of loads. The load is the amount of a given substance or pollutant for a given period of time. Using the key steps necessary for the development of a water quality monitoring and assessment programme, a framework has been developed that can be applied to water accounting projects using typical water accounting applications. Two examples of potential applications are used to consider the technical, institutional, and financial requirements. Implementing a framework for incorporating water quality monitoring and assessment into water accounting should contribute substantially to the need for more water quality data at global scale. Such data are required to facilitate achievement of Sustainable Development Goal 6 “Ensure availability and sustainable management of water and sanitation for all” through more efficient water resources management and greater awareness of water quality impacts in the agricultural water use sector.

Fault Tree Analysis of Trade-Offs between Environmental Flows and Agricultural Water Productivity in the Lake Urmia Sub-Basin Using Agent-Based Modeling

The recent problems of Lake Urmia (LU) are caused by extensive and complex socio-ecological factors that require a comprehensive approach to consider the relationships between users and identify failure factors at the basin level. For this purpose, an agent-based simulation model of farmers’ social interactions and economic interests (ABM) with various support scenarios and random supervision and training by the government agent is developed to evaluate its impact on independent farmers’ decision-making in the form of a complex adaptive system. Finally, a fault tree analysis (FTA) is created in the Cara-FaultTree 4.1. software to identify scenarios that lead to the non-development technology in irrigation management (non-DTIM) in the LU sub-basin. The assessment of the impact of government supervision and training revealed that the main causes of non-DTIM in the LU basin are a lack of demands from farmers and low awareness among residents of the basin, with failure probabilities of 0.90 and 0.86, respectively. Ultimately, the failure probability of the main event (non-DTIM) was 0.50. The paths of proper training and farmers’ requirements for sustainable agricultural water supply should become more stringent. The results confirm that appropriate measures to strengthen government supervision and training, as well as raise farmers’ awareness of the importance of long-term sustainability of water resources, can lead to greater resilience in the DTIM.

Evaluation of soft-computing techniques for pan evaporation estimation

Estimation of pan evaporation (Epan)  can be useful in judicious irrigation scheduling for enhancing agricultural water productivity. The aim of  present study was to assess the efficacy of state-of-the-art LSTM and ANN for daily Epan estimation using meteorological data. Besides this, the effect of static time-series (Julian date) as additional input variable was investigated on performance of soft-computing techniques. For this purpose,the models were trained, tested and validated with eight meteorological variables of 37 years by using preceding 1-, 3- and 5- days’ information. Data were partitioned into three groups as training (60%), testing (20%), and validation (20%) components. It was observed that the models performed well (best) with preceding 5-days meteorological information followed by 3-days and 1-day. However, all LSTMs simulated peak value of Epan was more accurate as compared to lower values. Meteorological data with julian date improved the performance of LSTMs (0.75<NSE 1; PBias< 10; KGE 0.75). The ANN trained using only meteorological data (preceding 5-days information) had better performance error statistics among all other ANNs and LSTMs with minimum MAE (0.68 to 0.86),  RMSE (0.93 to 1.22),  PBias (-0.73 to 2.44) and maximum NSE (0.83 to 0.84) and KGE (0.89 to 0.92). Overall, it was inferred that the forecasting of meteorological parameters using a few days preceding information along with Julian date as the time series variables resulted in better estimation of Epan for the study region.

Nutritional Water Productivity and Global Food Security

Water is life and is one of the major inputs for agriculture. Earth has a finite supply of fresh water and therefore, demands that every drop of annual rainfall should be conserved and judiciously utilized for production and post- production agriculture to get maximum nutrients per unit of water. The concept of water productivity in agriculture is now shifting from harvest index per unit of land and water to nutrients (protein, carbohydrate, fat, etc.) produced per unit of water. This varies with food commodities and locations. For example, the total dietary energy produced by potato, maize, peanut, wheat, milk, egg and beef using one m3of water are about 5600 kcal, 3800 kcal, 2300 kcal, 2280 kcal, 660 kcal, 520 kcal and 100 kcal, respectively. Similarly, the production of protein using one m3 of water by potato, peanut, maize, wheat, egg, milk, chicken, and beef are 150 g, 111 g, 77 g, 74 g, 41 g, 40 g, 33 g and 10 g, respectively. This paper describes the water nutrient productivity of some of the crops and livestock products and suggests as to how to provide food and nutritional security through an appropriate and balanced diet design, to the maximum number of people of the world from the limited and dwindling land, water and bio resources.

Water footprints and crop water use of 175 individual crops for 1990–2019 simulated with a global crop model

The water footprint of a crop (WF) is a common metric for assessing agricultural water consumption and productivity. To provide an update and methodological enhancement of existing WF datasets, we apply a global process-based crop model to quantify consumptive WFs of 175 individual crops at a 5 arcminute resolution over the 1990–2019 period. This model simulates the daily crop growth and vertical water balance considering local environmental conditions, crop characteristics, and farm management. We partition WFs into green (water from precipitation) and blue (from irrigation or capillary rise), and differentiate between rainfed and irrigated production systems. The outputs include gridded datasets and national averages for unit water footprints (expressed in m3 t−1 yr−1), water footprints of production (m3 yr−1), and crop water use (mm yr−1). We compare our estimates to other global studies covering different historical periods and methodological approaches. Provided outputs can offer insights into spatial and temporal patterns of agricultural water consumption and serve as inputs for further virtual water trade studies, life cycle and water footprint assessments.

Assessing the value of deep reinforcement learning for irrigation scheduling

Due to increasing global water scarcity pressure, researchers, policy makers and industry are looking for innovative solutions to increasing agricultural water productivity. Motivated by recent success within complex decision-making environments, Deep Reinforcement Learning (DRL) is being proposed as a method for optimizing irrigation strategies. Early research has hinted towards increased profits with DRL compared to heuristic approaches such as soil-moisture thresholds or fixed schedules. However, an assessment of the value of DRL for irrigation scheduling that incorporates local climate variability and water-use restrictions has yet to be performed. To address this gap in the literature, we created aquacrop-gym, an open-source Python framework for researchers to train and evaluate customized irrigation strategies within the crop-water model AquaCrop-OSPy. In this analysis, aquacrop-gym was used to quantify the value of DRL in comparison to conventional irrigation scheduling techniques (e.g., optimized soil-moisture heuristic) for maize production in an intensively irrigated region of the central United States. The DRL and heuristic approaches were both trained on 70 years of weather data produced from the weather generator LARS-WG, and evaluated on 30 unseen validation years of generated weather data. Findings from this analysis show that in the presence of high rainfall variability, DRL does not outperform conventional optimized heuristics. However, in the scenario where rainfall is set to zero, DRL approaches achieve higher profits on the unseen validation years. Similarly, DRL approaches also outperform optimized heuristics when severe water-use restrictions are introduced. Our analysis demonstrates that DRL approaches are a promising method of irrigation scheduling, notably in regions where farmers are faced with significant physical or regulatory water scarcity.

Agricultural Water Productivity Using Weighted Aggregated Sum Product Assessment Method

Water productivity is a unit of agricultural production for water decrease in the size, of crops, and trees, for livestock and fish can be evaluated. Agriculture water productivity per hectare tons of water or kg consumed kg of water the yield is consumed following) yields on the unit size of the water physical productivity disclosure of crop yields total or net present the yield period will change by value water losses or economic productivity incomplete or excluded water consumption with reference volume ( a bank water ) per unit block. Water productivity ‘ per unit crop production ’ will be used and defined as a water's number. Agricultural production systems with water the idea that productivity is ‘ the same more food with water bodies focusing on manufacturing ’ or with ‘ low water bodies production of the same amount of food does. Initially, irrigation performance of methods irrigation ability to describe or water use capacity was used. In terms of agriculture, ‘ water use capacity ’ will produce a plant as the quantity of organic matter is defined. This is, however, the terms used ‘ water use capacity ’ and ‘ performance ’ will not follow the classical concept of it, this is for input and output uses the same units. Therefore, the international water management company ( iwmi ) ‘ water use capacity ’ to ‘ water productivity ’ until the change of nomenclature is proposed. Analysis purpose, size, and domain step water productivity are many you can define more ways. one unit of water revenue equals one unit of water productivity (wp). More food, more money, a better standard of living, and improved ecosystem services result from increased water productivity. One unit of water revenue equals one unit of water productivity (wp). More food, more money, a better standard of living, and improved ecosystem services result from increased water productivity. It aims to produce contains. Crop, livestock, and water of fisheries in the domains of productivity to improve the size of the basin there is considerable opportunity. This will be used to achieve water harvesting in procedures, sub-watering, lack irrigation, precision techniques for irrigation, and practices for protecting soil and water are included. Soil fertility, pest, and disease management, and improved crop selection getting into markets from improvements like such by the interactive effects obtained water management impacts water directly with productivity related procedures. Benefits of using the Waspas technique weighted sum model (WSM) and weighted product model (WPM). Accuracy of ranking waspas alternatives is increased by integrating wsm and wpm. Wasps are an optimal at this point in the calculation of the additive parameter, which will be discussed in more depth later. The finest solution is the waspas method of analysis, which is outstanding. The long-range solution identifies a better answer than the short distance and negative-best, but a comparison of these distances is not thought to be important. Alternative is Kilograms per cubic meter, Dollars per cubic meter, Protein grams per cubic meter, Calories per cubic meter. Evaluation preference is Cereal Wheat ($0.2 per kilogram), Rice ($0.31 per kilogram), Maize ($0.11 per kilogram), Legumes Lentils ($0.3 per kilogram), Fava beans ($0.3 per kilogram). From the result it is seen that Rice ($0.31 per kilogram) is got the first rank where as is the Fava beans ($0.3 per kilogram) is having the lowest rank. .Rice ($0.31 per kilogram) is ranked first and Fava beans ($0.3 per kilogram) are ranked lowest.

Agricultural Water Footprints and Productivity in the Colorado River Basin

The Colorado River provides water to 40 million people in the U.S. Southwest, with river basin spanning 250,000 square miles (647,497 km2). Quantitative water rights assigned to U.S. states, Mexico, and tribes in the Colorado Basin exceed annual streamflows. Climate change is expected to limit streamflows further. To balance water demands with supplies, unprecedented water-use cutbacks have been proposed, primarily for agriculture, which consumes more than 60% of the Basin’s water. This study develops county-level, Basin-wide measures of agricultural economic water productivity, water footprints, and irrigation cash rent premiums, to inform conservation programs and compensation schemes. These measures identify areas where conservation costs in terms of foregone crop production or farm income are high or low. Crop sales averaged USD 814 per acre foot (AF) (USD 0.66/m3) of water consumed in the Lower Basin and 131 USD/AF (USD 0.11/m3) in the Upper Basin. Crop sales minus crop-specific input costs averaged 485 USD/AF (USD 0.39/m3) in the Lower Basin and 93 USD/AF (USD 0.08 per m3) in the Upper Basin. The blue water footprint (BWF) was 1.2 AF/USD 1K (1480 m3/USD1K) of water per thousand dollars of crop sales in the Lower Basin and 7.6 AF/USD 1K (9374 m3/USD1K) in the Upper Basin. Counties with higher water consumption per acre have a lower BWF.

Agricultural eco-efficiency and water footprint- A case study of fifteen crops in the Chupaca province of  Peru

The water footprint is an indicator of the impact of water use from its formation to its final destination. Agricultural eco-efficiency measures the efficient use of resources or materials available for crop production. Water's economic productivity analyses a product's efficient value as per its water supply and commercial value. The present research aimed to determine and relate the water footprint, economic productivity of water and agricultural eco-efficiency of 15 crops in the province of Chupaca - Peru. Georeferencing material was used for the delimitation of agricultural species, CROPWAT 8.0, CLIMWAT8.0, ArcGis 10.5 software, mathematical equations for the water footprint, agricultural eco-efficiency (Data Envelopment Analysis (DEA)) and economic productivity of water. The Total water footprint (TWF) of the fifteen crops was 1718237.01 m3/ton, likewise the BlueWF > GreenWF > GreyWF. In their economic outputs, gross value of production (GVP) > agricultural production (Ag-p) > economic rent agricultural (ERA) was verified. In environmental costs, water consumption that meets the needs of crops (Wc-Ag) > consumption of phytosanitary products (C-fly) > fertilizer consumption (C-fe) was determined. The average Agricultural eco-efficiency (Ag-Eec) and Economic water productivity (Ewp) were 89.8% and 0.046 PEN/m3 respectively. Statistical analysis between Ewp and Ag-Eec was rho = 0.18, t-test = 0.66 < 2.16 (α = 0.05; bilateral), and the correlation indicated that both activities are independent. The environmental costs and economic outputs of agricultural eco-efficiency did not influence the economic value of water.  

A new framework for evaluating water use reduction strategies using an integrated, holistic, and transparent approach (Urmia Lake basin case study)

Agriculture and food security have always faced challenges related to water scarcity, so agricultural water conservation is the most important strategy for overcoming this challenge. In this study, the REWAS tool developed by FAO and Future Water was used to evaluate the effects of technological and management strategies on real water savings (RWS) in the Zarrineh River sub-basin, Iran. Nine farms were investigated (wheat, corn, tomato, sugar beet), including control and treatment farms. According to changes in water consumption and return flows, RWS was calculated for various scenarios of field interventions. To assess the water consumption in Basin scale, REWAS was simplified the accounting framework for water consumption (consumed and unconsumed). In the Zarrineh River sub-basin, the use of the techniques applied in the treatment farm, compared to the control farm (according to farmer customary practices), resulted in a significant reduction of non-beneficial consumption. For example, according to the REWAS evaluation in West s17 farn, the treatment farm consumed the most useful water compared to the control farm (331 mm and 267 mm, respectively), which led to the most useful water consumption at basin scale (to the order of 3920 mm and 3166 mm). According to the results on the scale of the basin , the highest amount of recoverable discharge was also obtained in the control farm with an increase of 3774 MCM over the treatment farm. Using strategies to reduce water consumption resulted in apparent water saving (AWS) at the farm scale, but these AWS were very different from their RWS at the basin scale and the RWS at the basin scale (13%) was much lower than the AWS at the farm scale (38%). According to the results, apparent water savings (AWS) at the farm scale do not reduce water consumption, and may actually increase it when viewed in a basin scale. REWAS concepts indicate that significant amounts of “lost” water are actually recoverable return flows that can be used by downstream users or contribute to wetlands. According to REWAS concepts, real water savings can only be achieved by preventing non-recyclable backflows. Assessing RWS can help decision makers select sustainable interventions that will improve agricultural water productivity by providing information on farm flows, irrigation networks, and basin scales.

A Review of Nutritional Water Productivity (NWP) in Agriculture: Why It Is Promoted and How It Is Assessed?

Assessment of nutritional water productivity (NWP) combines a metric of crop or livestock production per unit water consumed and human nutritional value of the food produced. As such, it can rationalize the use of scarce water for a portfolio of crop and livestock production systems that jointly match human nutritional needs and reduce water scarcity impacts. However, a comprehensive search and review of 40 NWP studies highlighted that current NWP studies vary widely in terms of their methodological approaches, the data and tools used and the water flows and nutrient content accounted for. Most of the studies accounted for evapotranspiration stemming from precipitation and technical water, and/or inclusion of the withdrawn technical water. Water scarcity was only addressed in four studies. The reported NWP values also varied for accounting of macro- (energy, protein, fat and carbohydrates) and micro-nutrient (minerals and vitamins) content. The methodological differences, however, severely limit the informative value of reported NWP values. A multidisciplinary research effort is required to further develop standardized metrics for NWP, including its local environmental water scarcity impacts. A robust NWP analysis framework in agriculture should focus on the integration of assessments of NWP and water scarcity impact (WSI), and development of more field measurements and locally calibrated and validated agrohydrological and farm production models to quantify reliable NWP values and their associated WSI of agriculture production systems worldwide.

Evaluation of water productivity of smallholder irrigation along a Sand River, in the Maigobo watershed of northern Ethiopia

Sand dams are a type of rainwater harvesting technology that can increase groundwater recharge in dry land environments. They are constructed across seasonal sandy rivers and trap sand and water during floods. The sand acts as a natural filter and storage medium for the water, which can be extracted by the local communities through wells, pumps, or scoop holes. This research aims to evaluate agricultural water productivity based on water consumption (ETa) and biomass production of smallholder irrigation schemes along a sand river with a sand dam in the riverbeds of northern Ethiopia. Remotely sensed satellite data, WaPOR (Water Productivity through Open access of Remotely sensed derived data) database was used to estimate water productivity in the arid and semi-arid area. Following the installation of the sand dam at the irrigated parts of the Mai Gobo watershed, both the annual and seasonal values increased over a number of years, according to the spatial analysis of ETa and biomass production. For instance, the maximum seasonal ETa before the dam was about 70 mm/season, whereas after the dam the spatially analysed ETa reached 174 mm/season. Likewise, maximum seasonal biomass production of 1117 kg/ha and 4433 kg/ha were obtained before and after the construction of the sand dam, respectively. Agricultural water productivity at the irrigated plots of the Mai Gobo watershed was also improved from 1.4 kg/m3 to 2.9 kg/m3 before and after the sand dam respectively. Generally, the output of the study can be used as an input for the concerned stakeholder to take measures against the water scarcity problem in the study area. The study also contributes to the scientific knowledge and understanding of the hydrological and agronomical impacts of the sand dam in the arid and semi-arid regions.

Water rights reform and water-saving irrigation: evidence from China

Abstract As a market-based water resource management, the water rights reform (WRR) will allocate water rights to water users and allow water users to trade water rights, which can realize the reallocation across water users. In this context, the adoption of water-saving irrigation (WSI) is an important technical form to adapt to the reform. Based on this, this paper studies the impacts of the WRR on WSI using the difference-in-differences (DID) strategy. The results show that the WRR could increase the land area for WSI by an average of 13.63%. The WRR could promote the expansion of high-efficiency irrigation mainly because the WRR could promote the expansion of spray and drip irrigation areas, and micro-irrigation land areas, which are high-efficiency water-saving irrigation technologies. In addition, the WRR also could improve agricultural production by increasing agricultural water productivity and planting area (including the sown area of grain crops and cash crops), but the WRR does not reduce agricultural water extraction. Therefore, the WRR could increase agricultural production without increasing agricultural water extraction.

Practical approaches to enhance water productivity at the farm level in Asia: A review

AbstractThe global population is constantly increasing, reached 8 billion in November 2022 and is expected to reach 9 billion by 2037. This increased population is expected to increase the demand for food, clothing and shelter, which in turn are heavily dependent on limited water resources. The available freshwater resources for agricultural use are further declining due to overexploitation and changing climate in the major food baskets of the world. This increasing water scarcity is exacerbated by expanding cities due to increasing urbanization. This calls for a new look at the allocation of water to agriculture. Therefore, the development of new strategies to improve agricultural water use may serve as an important adaptation strategy. This review attempts to include a comprehensive review of the literature on (i) the status and definition of water productivity and (ii) factors responsible for low water productivity (WP) in Asian agriculture. Furthermore, it contains practical approaches to enhance water use efficiency at the farm level covering all field crops and a range of soil types, which include (i) agronomic interventions; (ii) genetic interventions, such as the identification and cultivation of crop cultivars with high WP; and (iii) genotype, environment and crop management interactions for higher WP.

Exploring deficit irrigation as a water conservation strategy: Insights from field experiments and model simulation

Deficit irrigation has emerged as a viable approach to increase agricultural water productivity. The objectives of this study were to 1) develop water conservation strategies by investigating responses of green bean (Phaseolus vulgaris) and sweet corn (Zea mays var. saccharata) to four irrigation treatments and 2) evaluate the performance of the Decision Support System for Agrotechnology Transfer (DSSAT) model in simulating growth and yield responses of green bean and sweet corn under four irrigation treatments. A field experiment was conducted using 32 experimental plots, established under a linear move sprinkler irrigation system equipped with variable rate irrigation (VRI) technology, at the Tropical Research and Education Center (TREC), Homestead for two cropping seasons between November and March in 2020–2021 and 2021–2022. The experiment involved four irrigation treatments: full irrigation (FI) (T4), and three levels of deficit irrigation, 75% FI (T3), 50% FI (T2), and 25% FI (T1) with four replications arranged in a completely randomized block design (RCBD) for each crop. The average soil moisture content of the full irrigation treatments was maintained above the management allowable depletion (MAD). Field data collection included various parameters of the crops including plant phenology, plant height, canopy width, fresh and dry biomass, and yield. Results confirmed that all three deficit irrigation treatments did not significantly affect the yield and yield components of the two crops during both seasons compared to the T4. This finding revealed that the highest crop water productivity was achieved from T1, 38.3 and 41.4 kg m−3 for green bean and 54 and 53 kg m−3 for sweet corn during the 2020–2021 and 2021–2022 seasons and up to 75% irrigation water saving could be achieved without affecting plant growth and yield. The DSSAT model performed well in simulating yield and yield components for the two crops in response to the four irrigation treatments. Model evaluation results showed that the minimum mean absolute error (MAE) for green bean fresh pod yield simulation was 2.1 Mg ha−1 with an index of agreement (d-Stat) of 0.6, while for biomass, it was 0.24 Mg ha−1 with a d-Stat of 1. Similarly, for sweet corn, the model simulated fresh cob yield with a minimum MAE of 6.51 Mg ha−1 and d-Stat of 0.9 and biomass with MAE of 1.63 Mg ha−1 and d-Stat of 0.9. Overall, implementing deficit irrigation in green bean and sweet corn fields could result in considerable water savings while achieving acceptable crop yield. The DSSAT model could be also a useful tool to simulate green bean and sweet corn responses to different irrigation scenarios and aid water management decisions under South Florida conditions.

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

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

Role of China's agricultural water policy reforms and production technology heterogeneity on agriculture water usage efficiency and total factor productivity change

Role of China's agricultural water policy reforms and production technology heterogeneity on agriculture water usage efficiency and total factor productivity change

Optimizing the Criteria of Crop Pattern Based on Sustainable Development and Increasing Agricultural Water Productivity in Tajan Catchment Basin

Optimizing the Criteria of Crop Pattern Based on Sustainable Development and Increasing Agricultural Water Productivity in Tajan Catchment Basin

Agronomic analysis of WaPOR applications: Confirming conservative biomass water productivity in inherent and climatological variance of WaPOR data outputs

CONTEXTImprovements in agricultural water productivity with constrained water resources are often regarded a prerequisite to meet food demands of a growing world population. The WaPOR data portal was launched to monitor biomass, evapotranspiration and biomass water productivity in Africa and the Near East using remote sensing technologies. The WaPOR database shows spatial pixel variation in biomass, suggesting scope to improve water productivity at field level. OBJECTIVEThe aim of this paper is to assess with regression analyses for different spatial and temporal scales whether spatial variability in biomass and evapotranspiration as revealed by WaPOR can be attributed to human influenceable factors, variations in local climate, or methodologically inherent inacuracies of the WaPOR data. METHODSVariation in biomass and evapotranspiration data was assessed through agronomic linear regression analyses, for two large-scale irrigated sugarcane estates in Ethiopia (Wonji) and Mozambique (Xinavane). RESULTS AND CONCLUSIONSIn these cases 82–94% of the variation in biomass and evapotranspiration is attributed to crop photosynthetic efficiency (very large influence), local climate (large influence) and irrigation technology (small influence). The remaining unexplained spatial variability is small (6–18%) and falls within an error range of +/− 9%. In conclusion, WaPOR performed very well by neatly reproducing the conservative relationship between biomass and evapotranspiration, which also means there is very limited scope to improve biomass water productivity through WaPOR monitoring. Further research is recommended on the magnitude of WaPOR accuracy and other sources that explain variations in biomass and evapotranspiration. SIGNIFCANCEApplicability of the WaPOR database to monitor biomass water productivity was assessed. Spatial variability in biomass and evapotranspiration data largely stemmed from photosynthesis and local climate, factors farmers and water managers can hardly influence.