Crop Water Production Function Research Articles

Water Resource Demand Analysis in the Loess Plateau Area Based on Food Security: A Case Study of Changwu County in Shaanxi Province

The shortage of water resources has been a major problem in the Loess Plateau. Under the situation of a steady increase in population, cultivated land gradation and rapid development of regional economy, how to achieve food security has been becoming an important issue in this region. Thus, a typical case study of Changwu County in Shaanxi Province was conducted. With the help of grey correlation analysis method, it concluded that the water resource is the leading factor in limiting the local food safety. Furthermore, the water consumption and water demand at various sectors are compared and analyzed. Then, the crop-water production function was introduced to optimize the irrigation water allocation. The paper finally discussed the development strategy on water resources utilization and food security, which would provide a scientific basis for the water resources exploitation and be a guarantee for the food security in Changwu County.

Maize Yield as a Function of Water Availability across Precipitation Years in the North China Plain

Crop water production functions require precise knowledge of crop productivity across a wide range of soil water availability (WA). Field studies have demonstrated the yield response of maize (Zea mays L.) to WA, but the relationship has not been quantified across various precipitation years in the North China Plain (NCP). This study was conducted to investigate the effects of the simulated interannual variability in precipitation on maize yield in a winter wheat (Triticum aestivum L.) –summer maize double‐cropping system in the NCP, in 2014 and 2015. A rainfall simulator system was used to simulate contrasting precipitation years, including wet, normal, normal‐dry, and dry years. A fixed amount of 70 mm irrigation was applied at different growth stages of maize, including at planting (I70), at planting and tasseling (VT) stages (I140), and at planting, VT, and milk stages (I210). The objective was to quantify the relationships among crop production, water use, and WA for maize. We found that I210 improved soil water storage (SWS, mm) by 19 to 36% in the midseason of normal‐dry and dry years but had no effect in normal and wet years, compared with I70. Crop evapotranspiration (ETc, mm d−1) was least in the dry year with the I70 treatment and differed with maize growth phases, with the peak occurring midseason for maize in both years. Regression analyses showed that ETc was negatively related to SWS but positively related to WA. A quadratic function described grain yield responses to WA and ETc, with total WA of 478 mm and daily mean ETc of 5.1 mm d−1 producing greatest grain yield. With yield response function acquired, we conclude that target maximum yield of 11.6 Mg ha−1 can be achieved after probability of exceedance of precipitation equals 18 with irrigation water requirement applied.

Yield Response of Mediterranean Rangelands under a Changing Climate

AbstractUnderstanding the Mediterranean rangelands degradation trends is a key element of mitigating their vulnerability and enhancing their resilience. Climate change and its inherent effects on mean temperature and the precipitation variability can regulate the magnitude, frequency and duration of droughts and aridity with a profound effect on ecosystem productivity. Here we investigate the effects of climate change to project the development of vegetation in the Mediterranean rangelands by (i) estimating the relative Standardized Precipitation Index and a modification of the United Nations Environment Programme Aridity Index to classify climate variability, and (ii) modelling vegetation response to climate using the Food and Agriculture Organisation crop–water production function. Climate model data are obtained from nine general circulation models under Relative Concentration Pathways 2.6 and 8.5 of the fifth phase of the Coupled Model Intercomparison Project. After correcting climate model data for biases, results for two 40‐year future study periods are compared with the baseline period 1961–2000 within a domain that includes the European Mediterranean. We show that a gradual but robust increase of aridity and drought frequency is estimated for most of the Mediterranean region, impacting rangeland vegetation yields. Projected drought and aridity disturbances may well represent permanent shifts to a warmer and more frequently dry status. This alternative stability of climatic pressure lies outside the limits of ecosystem resilience and may indicate that in some cases vegetation will either adapt to the new conditions or be succeeded by more water‐stress tolerant species. Results raise concerns about the fate of the Mediterranean rangelands and the effectiveness of mitigation measures. Copyright © 2017 John Wiley & Sons, Ltd.

An interval multistage water allocation model for crop different growth stages under inputs uncertainty

Due to different responses of crop growth stages to the water deficit, it is necessary to optimize water allocation between different growth stages to obtain the maximum food production in reservoir irrigation systems which are widely distributed throughout Southern China and India. In order to address the inputs uncertainties and dynamics existing in the above agricultural water management, an interval multistage water allocation model is developed. By incorporating multistage stochastic programming and interval parameter programming, the developed model can deal with uncertain inputs both expressed as interval parameters and probability distributions, and realize a dynamic irrigation among different growth stages from a reservoir. In the model, water requirement targets are first treated as first-stage decision variables to tackle the unique problem of agricultural water management. Additionally, given that net benefit and penalty of each growth stage are key parameters due to their determinative roles for allocation between different growth stages, a crop water production function is introduced into the calculation to make them factually reflect the competition among different growth stages. The model is then applied to the Yangshudang Irrigation District to plan rice irrigation and demonstrate its applicability. Rainfall has been divided into five levels with probability distributions in each growth stage and parameters have been characterized as interval numbers to show system uncertainty. Five scenarios that represent different initial active storage levels of the reservoir are set to acquire more detailed results. Through the parameter estimation, net benefits are [1.08, 1.29], [5.04, 6.01], [11.79, 14.08] and [1.61, 1.92] RMB/m3, and penalties are [2.39, 2.48], [11.13, 11.54], [26.05, 27.01] and [3.55, 3.68] RMB/m3 for tillering stage, booting stage, heading stage and milky stage respectively. Through the model simulation, water requirement targets in booting stage and heading stage under all scenarios are set at their upper bound, while this figure in tillering stage reaches its upper bound only when initial active storage is under high or very high level. The results show that irrigation water can be optimally allocated between different growth stages of a single crop in a single reservoir system under inputs uncertainty. Although there is a limitation to regard rainfall as to be uniform in the whole area, the solutions of water requirement targets under different scenarios, as well as water allocation patterns among different growth stages, are valuable for optimizing irrigation water use in meso- and micro-scale agricultural system under inputs uncertainty.

Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model

Water scarcity has been a crucial issue for sustainable irrigated agriculture in the arid regions. In these regions where conserving water is paramount, optimal allocation and utilization of irrigation water is particularly important. In this study, a process-based regional economic optimization (PBREOP) model was developed for maximizing irrigation water use efficiency and economic benefit of an irrigation system. The PBREOP model is a two-level optimization model with combined use of an agro-hydrological model (SWAP-EPIC). The first level (farm scale) dealt with the optimal distribution of irrigation water and cropping pattern considering various crops and soils in a subsystem, using a non-linear programing technique. The second level (district scale) sought out the optimal strategy for irrigation water allocation among different subsystems using a dynamic programing algorithm. The crop water production functions (CWPFs) were an important component of the first-level objective function. They were derived with the SWAP-EPIC model considering different irrigation alternatives. The model was solved using the decomposition-harmonization method for large systems. The Yingke Irrigation District (YID) in the middle Heihe River basin, Northwest China was used as a case to test the PBREOP model. Nine CWPFs for three major crops and three major soils were firstly derived based on the simulations of different irrigation levels and climate conditions (20 years). Next, the PBREOP model for YID was established with 11 subsystems, and applied to the irrigation water use optimization under five water supply scenarios. Results showed that the total economic benefit in YID could be increased by 15% on average through the optimization of water allocation and cropping pattern with the same water supply amount as that of the current situation. A variation range of the risk was also obtained with considering the impacts of climate uncertainties. Scenario analysis showed that the total irrigation water could be reduced by 23% on average without benefit reduction when compared to the benefit of the present situation. Model test indicated that the proposed PBREOP model can efficiently optimize irrigation water use and cropping pattern on a regional scale.

Crop kites: Determining crop-water production functions using crop coefficients and sensitivity indices

The crop-water production function quantitatively evaluates the relationship between seasonal water use and crop yield and is used to evaluate optimal irrigation depth and assess the potential of deficit and supplemental irrigation. A simple and easily applicable methodology to develop crop- and region-specific crop-water production functions using crop coefficients and sensitivity-indices is presented. Previous efforts to describe the crop-water production function have not accounted for the effects of the temporal distribution of water use and trivialize the associated variability in yields by assuming an optimized or arbitrary temporal distribution. The temporal distribution of water use throughout the growing season can significantly influence crop yield, and the ability of farmers to manage both the timing and amount of irrigation water may result in higher yields. We propose crop kites, a tool that explicitly acknowledges crop yield as a function of the temporal distribution of water use to both evaluate the complete space of water use and crop yield relationships, and extract from this space specific crop-water production functions. An example for winter wheat is presented using previously validated crop-specific sensitivity indices. Crop-water production functions are extracted from the crop kite related to specific irrigation schedules and temporal distributions of water use. Crop-water production functions associated with maximizing agricultural production agree with previous efforts characterizing the shape as a diminishing curvilinear function. Crop kites provide the tools for water managers and policy makers to evaluate crop- and region-specific agricultural production as it relates to water management and the associated economics, and to determine appropriate policies for developing and supporting the infrastructure to increase water productivity.

Evapotranspiration and Yield of Eggplant under Salinity and Water Deficit: A Comparison between Greenhouse and Outdoor Cultivation

Cultivation environment can be effective on the degree of limitations in crop evapotranspiration and yield, as a result of water shortage and salinity. The purpose of this study was to determine and compare the impact of different irrigation regimes (daily, weekly and every two weeks) combined with different water salinities (0.8, 2.5, 5.0 and, 7.0 dS m-1) on eggplant yield (Y) and evapotranspiration (ETc) in outdoor and greenhouse cultivation. Daily ETc values were measured by diurnal weighting of microlysimeters throughout the growing season (from May 19th to September 5th, 2012 and June 1st to September 22nd, 2013) placed in a plastic greenhouse and outdoor basins. Measurements showed apparent variations between different irrigation regimes×water salinity treatments, during the early growing season in both years. Both water deficit and salinity factors had significant effects on ETc, ECe, Y, fruit diameter and shoot dry weight in both environments. The applicability of Doorenbos-Kassam linear crop-water production function along with Maas-Hoffman salt tolerance model was investigated in the greenhouse and outdoor conditions. The Ky coefficient obtained for outdoor and greenhouse eggplants treatments were 0.97 and 1.03 in the first year and 0.91 and 0.93 in the second year, respectively. Higher sensitivity of greenhouse eggplants to salinity was later demonstrated for both years, obtaining higher values of b and lower values of ECethreshold in the greenhouse eggplants.

Response of yield and water use efficiency to different irrigation levels at different growth stages of Kenaf and crop water production function

To provide a rational irrigation management for high yield cultivation of Kenaf, the effects of three irrigation levels at the three growth stages on raw fiber yield, shoot dry mass and water use efficiency of two Kenaf varieties were investigated, and crop water production function was simulated by the Jensen’s model. Two Kenaf varieties (V) included hybrid variety (Hongyou 2, V1) and conventional variety (Fuhong 992, V2). Three irrigation levels at different growth stages included (1) high water level (A1), middle water level (A2) and low water level (A3) at the seedling stage, (2) high water level (B1), middle water level (B2) and low water level (B3) at the vigorous growth stage, and (3) high water level (C1), middle water level (C2) and low water level (C3) at the flowering and fruiting stage. Orthogonal test with replicates, unequal level and the existence of interaction was designed. Orthogonal test indicates that significant effects of variety and irrigation level at the vigorous growth and flowering and fruiting stages on raw fiber yield were found, and the raw fiber yield of Hongyou 2 was significantly higher than that of Fuhong 992. The best combination of two varieties and three irrigation levels at the three growth stages was V1A2B1C1 for raw fiber yield and shoot dry mass and V1A3B1C3 for water use efficiency on the basis of dry raw fiber yield (WUErf). Simulation results of the Jensen’s model show that the sensitivity coefficient (λ2) was the highest at the vigorous growth stage, and significantly higher than those of other growth stages, showing that Kenaf was the most sensitive to water deficit at the vigorous growth stage, and the vigorous growth stage was the critical stage of water requirement for Kenaf. For Hongyou 2 and Fuhong 992, raw fiber yield was improved with the increase of irrigation amount at the vigorous growth stage and flowering and fruiting stage, and the simulation of the Jensen’s model also proved that Kenaf was very sensitive to water deficit at the vigorous growth stage, so Kenaf should be supplied by adequate irrigation at the vigorous growth stage. The raw fiber yield of two varieties had the highest at A2B1C1, but Honyou 2 had more raw fiber yield. As the response of Fuhong 992 to irrigation level at different growth stages was not more significant than that of Hongyou 2, Hongyou 2 should be planted under better water and fertilizer condition.

Determining Optimal Seasonal Irrigation Depth Based on Field Irrigation Uniformity and Economic Evaluations: Application for Onion Crop

The crop water production function (WPF), representing the relationship between crop yield and seasonal irrigation water, is a useful tool for irrigation planning purposes. The objective of the paper is to propose a methodology to evaluate the optimal seasonal irrigation depth based on the crop production function, the field distribution uniformity, and economic considerations. An extended unpublished database experimentally obtained on the onion crop on the island of Kula, Hawaii, was initially used to assess the crop WPF. The combination between the crop WPF and the model representing the field distribution uniformity allowed determining the area subjected to underirrigation and overirrigation, as well as the corresponding yield, that were finally averaged across the field. An economic comparison was also carried out in order to evaluate the optimal seasonal water depth aimed at maximizing the farmer’s gross margin under different irrigation system distribution uniformities and water prices. According to the experimental data, it was observed that the onion crop is more sensitive to deficit than overirrigation, as well as that a quadratic model, valid for the entire range of the seasonal applied irrigation depths fitted better than a two-slope linear model, representing separately the conditions of deficit and over-irrigation. Moreover, the maximum yield, as well as maximum gross margin, can be obtained by applying average irrigation depths lower than those which correspond to the maximum yield, with percentages that declined at decreasing of water distribution coefficient and at increasing of water price. The proposed methodology can be applied even for other crops once the corresponding WPFs aree known, thus providing interesting evaluations that are useful for irrigation planning.

Integrated management of water resources demand and supply in irrigated agriculture from plot to regional scale

Abstract. Growing water scarcity in agriculture is an increasing problem in future in many regions of the world. Recent trends of weather extremes in Saxony, Germany also enhance drought risks for agricultural production. In addition, signals of longer and more intense drought conditions during the vegetation period can be found in future regional climate scenarios for Saxony. However, those climate predictions are associated with high uncertainty and therefore, e.g. stochastic methods are required to analyze the impact of changing climate patterns on future crop water requirements and water availability. For assessing irrigation as a measure to increase agricultural water security a generalized stochastic approach for a spatial distributed estimation of future irrigation water demand is proposed, which ensures safe yields and a high water productivity at the same time. The developed concept of stochastic crop water production functions (SCWPF) can serve as a central decision support tool for both, (i) a cost benefit analysis of farm irrigation modernization on a local scale and (ii) a regional water demand management using a multi-scale approach for modeling and implementation. The new approach is applied using the example of a case study in Saxony, which is dealing with the sustainable management of future irrigation water demands and its implementation.

Crop Water Production Functions—A Review of Available Mathematical Method

<p>Crop water production function is the relationship between obtained marketable yield and the total amount of water used by plant through evapotranspiration. Different mathematical expressions have been derived from field experiments to predict yield knowing water use. Such expressions provide a powerful tool in water allocation optimization. However these functions are commonly confused with the relationship between available water and crop yield. This paper reviews functions that describe crop water production, the context they can be used for, and their strength and weakness.</p>

Water productivity and water footprints are not helpful in determining optimal water allocations or efficient management strategies

In a recent paper in Water International, Amarasinghe and Smakhtin suggest that crop–water production functions provide conceptual support for using water productivity and water footprints to guide water allocation and to determine optimal management strategies. Their analysis is thoughtful and interesting. Yet, there is no conceptual foundation supporting the use of water productivity or water footprints in an optimizing context. Production functions do not provide conceptual support. Policy makers wishing to achieve the optimal allocation of water must consider incremental gains and costs, including opportunity costs and externalities. Ratios depicting average output or average value do not contain sufficient information for determining optimal water allocations or efficient management strategies.

Productividad del agua en el cultivo de maíz en condiciones del sur de La Habana

The high level at which water affects agricultural production and its capability of the latter to feed populations leads to the need of studying the problem of water productivity. The study of the crop water production function and its application in planning water use is an important strategy to increase water productivity. Data of 12 experiments carried out at the Irrigation and Draining Research Station, located in Alquizar at south of Havana Province (red ferralitic soils), and regression tools were used to estimate the crop water productivity (WP) and crop yield response factor (Ky) for corn. Crop water productivity based on evapotranspiration (WPET) ranges between 0.9 and 2.5 kg•m-3, while the WPI was 16.43 kg•m-3 when 266.8 m3•ha-1 of irrigation water were applied. The range 0.86 to 2.9 kg•m-3 of WPT obtained for corn was in a wider range than the that reported by FAO for this crop. Every cubic meter of total water used in corn yield 1.93 kg. The slope (Ky) of the corn water productivity function was 1.67 in winter and 2.31 in summer. The relative loss of yield was proportional to irrigation deficit in both seasons. For a planned irrigation deficit of 20 % for corn it will expect a relative loss of 33.4 and 46.2 % of yield, in winter and summer respectively.

Developing and normalizing average corn crop water production functions across years and locations using a system model

Crop water production functions (CWPFs) are often expressed as crop yield vs. consumptive water use or irrigation water applied. CWPFs are helpful for optimizing management of limited water resources, but are site-specific and vary from year to year, especially when yield is expressed as a function of irrigation water applied. Designing limited irrigation practices requires deriving CWPFs from long-term field data to account for variation in precipitation and other climatic variables at a location. However, long-term field experimental data are seldom available. We developed location-specific (soil and climate) long-term averaged CWPFs for corn (Zea mays L.) using the Root Zone Water Quality Model (RZWQM2) and 20 years (1992–2011) of historical weather data from three counties of Colorado. Mean CWPFs as functions of crop evapotranspiration (ET), ET due to irrigation (ETa–d), irrigation (I), and plant water supply (PWS=effective rainfall+plant available water in the soil profile at planting+applied irrigation) were developed for three soil types at each location. Normalization of the developed CWPF across soils and climates was also developed. A Cobb–Douglas type response function was used to explain the mean yield responses to applied irrigations and extend the CWPFs for drip, sprinkler and surface irrigation methods, respectively, assuming irrigation application efficiencies of 95, 85 and 55%, respectively. The CWPFs developed for corn, and other crops, are being used in an optimizer program for decision support in limited irrigation water management in Colorado.

Calculation of Parameters of Crop Water Production Function of Jensen Model Based on Stochastic Particle Swarm Optimization Algorithm

In this paper, Stochastic Particle Swarm Optimization Algorithm (SPSO) is introduced to calculate parameters of crop water production function directly. SPSO is an improvement of the standard PSO, compared with which, it can be guaranteed to converge to the global optimization solution with probability one in theory, and speed up the convergence. The dimension of space of a particulate is determined by the phases of crop growth and development period in SPSO, and the sensitive index , an important parameter of crop water production function, is regarded as optimized variable to be calculated directly. This method can eliminate some problems existing in linear regression method, such as biased estimate and distortion of result and it can search the global optimization value more easily than the PSO. Through example analysis, we can see that the SPSO is better than PSO in accuracy and calculation speed.

Optimal Allocation of Water Resources Model for Different Growth Stages of Crops under Uncertainty

In this study, an integrated interval nonlinear programming model based on Jensen’s water production function is proposed for the optimal allocation of water resources under uncertainty in agricultural irrigation. The model provides an effective approach to solve the problems for crop irrigation schedules and to successfully deal with the inconsistencies in crop water requirements and rain capacity. The model can allocate the limited water to different growth stages of crops based on Jensen’s crop water production function, which further improves water use efficiency. The method provides the means to promote the development of water-saving irrigation and further improves the economic value of water resources. Two major advantages make the model unique in comparison to the other optimization techniques. First, it can solve the uncertainties of the agricultural water resources system, and the results are given in the form of intervals, which can provide more leeway for decision makers. Second, the rainfall was divided into 18 stages with 10 days per stage in this paper (each of the crop growth stages were matched with different stages); then, the limited water resources were optimally allocated to different crops in different growth stages based on Jensen’s model. The model not only increases the output per unit of water but also improves water use efficiency. In this paper, the Yongchang irrigation district in Wuwei was taken as an example to demonstrate the feasibility of the proposed model. The results are helpful for water resources managers in making decisions about water allocation and striking a balance between environmental and economic objectives.

Dynamic Crop Water Production Model of Plantain Yield Response to Water

Crop Water Production Function Model (CWPFM) has been studied for quite few decades and since recent years developing a non -time specific and non-site-specific CWPFM has been receiving more and more attentions.A dynamic model of plantain bunch yield response to daily available soil moisture is presented. Using a priori information on dry matter accumulation in the plant when water is not limiting and using a piecewise linear regression, functions of crop response to soil moisture from experimental test plot data were estimated.With the estimated model, the bunch yields for various irriga tion schedules were predicted.The predictions demonstrate the sensitivity of the model to the timing of irrigations.

Monthly Optimal Reservoirs Operation for Multicrop Deficit Irrigation under Fuzzy Stochastic Uncertainties

An uncertain monthly reservoirs operation and multicrop deficit irrigation model was proposed under conjunctive use of underground and surface water for water resources optimization management. The objective is to maximize the total crop yield of the entire irrigation districts. Meanwhile, ecological water remained for the downstream demand. Because of the shortage of water resources, the monthly crop water production function was adopted for multiperiod deficit irrigation management. The model reflects the characteristics of water resources repetitive transformation in typical inland rivers irrigation system. The model was used as an example for water resources optimization management in Shiyang River Basin, China. Uncertainties in reservoir management shown as fuzzy probability were treated through chance-constraint parameter for decision makers. Necessity of dominance(ND)was used to analyse the advantages of the method. The optimization results including reservoirs real-time operation policy, deficit irrigation management, and the available water resource allocation could be used to provide decision support for local irrigation management. Besides, the strategies obtained could help with the risk analysis of reservoirs operation stochastically.

Predicting crop-water production functions for long-term effects of deficit irrigation on citrus productivity (SW Spain)

Crop-water production functions (CWPFs) constitute a suitable tool for irrigation management in water-scarcity situations, and for estimating the yield response when a crop is subjected to long-term water stress. This work examines the long-term effects of different strategies of sustained deficit irrigation in mature sweet orange trees (Citrus sinensis L. Osb. cv. Salustiana). The trial was conducted from 2004 to 2010, with three deficit treatments imposed as a function of the water-stress index, which is defined as the ratio between the actual volume of water supply and the crop evapotranspiration rate. Crop-water production functions were evaluated for each year. Additionally, different yield-loss functions were defined on the basis of the impact of deficit irrigation for the entire study period. According to the findings, the crop response to water stress followed a clear trend over successive years, in which the time course of the yield loss showed an attenuated oscillation. Thus, the calculated yield-loss functions could be provided as an alternative tool for programming a long-term strategy of deficit irrigation under a water-scarcity situation.

INTEGRATED STEPWISE APPROACH FOR OPTIMAL WATER ALLOCATION IN IRRIGATION CANALS

ABSTRACTOptimal water management in irrigation networks should be an integrated approach which includes both canal and field systems. Integrated optimal water management of irrigation networks requires consideration of both optimal allocation of water among different crops at field level, and optimal water delivery scheduling at the canal level, which is often neglected. In this study, an integrated stepwise approach has been presented for considering both farm‐ and canal‐level optimization problems. Three modules are proposed for the optimization model. In the first module, crop–water production functions for different crops are produced, and optimal seasonal allocation of available water among several crops is determined using an LP algorithm. The objective function of this module is to maximize total net benefit of all crops. The second module takes the optimal allocated water to each crop from the first module, and determines its optimal distribution through the growing season for each crop, such that the total yield at the end of the season is maximized. Taking into account the output of the second module, and cultivated area of each crop, the water requirement volume for each secondary canal is computed. The third module determines optimal water delivery scheduling among outlets of the secondary canal using a genetic algorithm (GA). The objective of the third module is to minimize canal flow, and to maximize delivery adequacy and beneficial water delivery duration. The decision variables are delivery discharge to each outlet, the number of outlets grouped in a block which receive water sequentially, and the sequence of water delivery to the outlets in each block. The proposed approach was applied on the K canal of the Moghan Irrigation Network in the north‐west of Iran. The optimal water delivery scheduling of a secondary canal, based on the optimal water allocation among several crops, and intra‐seasonal irrigation scheduling are presented, which shows the utility of the integrated approach. Copyright © 2013 John Wiley & Sons, Ltd.