Optimal allocation of water from a single purpose reservoir to an irrigation project with pre-determined multiple cropping patterns

A model for optimal allocation of water from a single-purpose reservoir to an irrigation project with pre-determined multiple cropping patterns was developed. The model consisted of two modules: (I) the intra-seasonal allocation model (non-linear programming) which is used for allocation of water among different crops for a definite combination of state variables (inflow class, rainfall class, reservoir storage classes at the beginning and at the end of the season) for the non-dormant season to maximize total farm income; and (II) the seasonal allocation model (stochastic-dynamic programming) which is used for the convergent operating policy over seasons for optimal expected farm income over a year. The model was applied to Ardak reservoir dam (I.R. Iran) in an arid region. Low river inflow in the dormant season at the study area could not admit the reservoir class changes for specific combinations of state variables, and therefore resulted in a non-usable result. Imposing a fictitious positive relative net benefit for all possible combinations of reservoir class changes eliminated this problem. It was also shown that rainfall did not play a marked role in the study area, which is an arid region, and its stochastic nature can be removed from the model.     

Optimizing multiple cropping systems: Simulation studies

Tsai et al. (1987) developed a combined network optimization—simulation model for optimal sequencing of multiple cropping systems. Essentially, simulations were used to generate a deterministic activity network. Then an optimization technique (K Longest Path algorithm) was applied to solve optimal sequences of multiple cropping. Using north Florida as a study region, the model was utilized to investigate optimal multiple cropping sequences in an irrigated or non-irrigated field. The results indicated that, for a non-irrigated farm, winter wheat followed by either soybean, maize or peanut was the most profitable cropping rotation in a multiple cropping sequence. Especially favorable was the double cropping of wheat—peanut. For an irrigated farm, a peanut crop was found to be prominent. In the case where peanut was not considered in the rotation, inclusion of irrigated wheat—maize cropping could not be recommended as a profitable multiple cropping system. Instead, double cropping of maize—soybean was the main scheme under irrigation with the possible substitution of a wheat—soybean crop sequence. To obtain higher, stable net returns, a north Florida farmer with no irrigation capability should plan his production system according to multiple cropping sequences SQ2 or SQ4 in the study. Use of these results for real-time decision making requires that the optimization be evaluated to select each new crop using current farm status and future expected weather and market conditions.     

Optimal irrigation planning model for an existing storage based irrigation system in India

A weekly irrigation planning LP model is formulated for determining the optimal cropping pattern and reservoir water allocation for an existing storage based irrigation system in India. Objective of the model is maximization of net annual benefit from the project. In an irrigation planning of a storage based irrigation system, initial storage of the reservoir at the beginning of the reservoir operation, expected inflows into the reservoir during each intraseasonal period, capacity of channels, crop calendar and yield response to water deficit in each growth stage of crop play a vital role in deciding acreage and water allocation to each crop. The planning model takes into account yield response to water deficit in each intraseasonal period of the crop, expected weekly inflows entering into the reservoir, storage continuity of reservoir, land and water availability, equity of water allocation among sub areas and proportionate downstream river release. One year comprising of 52 weeks is considered as planning horizon. To account for uncertainty in water resources availability, the model is solved for four levels of reliability of weekly inflows entering into the reservoir (90%, 85%, 80% and 75%). Alternative optimal cropping patterns and weekly releases to crops grown in each sub area under each main canal are obtained for various states of initial storage at the beginning of reservoir operation and for various levels of weekly inflows into the reservoir. Results reveal the importance of initial state of reservoir storage for feasible solution and shows the impact on cropping pattern with the change in initial storage of reservoir for different levels of reliability of weekly inflows.     

Adapting to intersectoral transfers in the Zhanghe Irrigation System, China: Part II: Impacts of in-system storage on water balance and productivity

This paper investigates the impacts of farm ponds in a context of declining supplies in a major canal command within the Zhanghe Irrigation System (ZIS), in Central China. As dam supplies have been diverted to higher-valued uses (hydropower, cities and industry), farmers have responded by constructing small storages within their fields. These farm ponds have given them sufficient flexibility in water supply to practice varying forms of alternate wetting and drying irrigation for rice without compromising yields and incomes. Ponds are recharged by a combination of return flows from irrigation and runoff from catchment areas within the irrigated perimeter. Various scenarios of water supply incorporating the main reservoir, in-system reservoirs, farm ponds and irrigation practices were simulated using the OASIS model. OASIS integrates surface and groundwater flows, and contains a crop growth module to aggregate the impacts of different water management regimes. The modelling and sensitivity analysis show that further reductions in main reservoir supplies will have a negative effect on rice production in dry and average years, and that ponds have played a crucial role in adapting agriculture to reduced canal supplies. The flexibility allowed by the ponds has resulted in increased water productivity, except in high rainfall years, but net depletion has not decreased, as local supplies have substituted for water from the main reservoir. The study demonstrates the importance of properly accounting for return flows and the necessity to understand crop production in relation to the actual depletion of water (as evapotranspiration) within an irrigation system.     

Economic impacts on farm households due to water reallocation in China's Chaobai watershed

Our study area in the Chaobai watershed, upstream of Miyun Reservoir, has been undergoing agricultural water transfers to downstream municipal uses in Beijing. We examine the impacts of water reallocation on crop production and farmers’ income and discuss issues relating to current compensation mechanisms. We use data from a survey of 349 farm households and their farm plots in the upper Chaobai watershed within Hebei province. Water reallocation from upstream to downstream areas has reduced agricultural water supply and the area irrigated. Regression results show that in plots deprived of irrigation, maize yields decrease by 21% and crop revenues decline by 32%. On average, losing irrigation on one hectare of cultivated land reduces net crop income by 2422 yuan. We examine compensation arrangements and social equity for the major policies implemented in the region and we identify gaps between current compensation levels and farmers’ income and production losses. The current compensation received by farmers is generally lower than the losses incurred due to reduced irrigation. A more appropriate compensation mechanism is called for in future water transfers.     

Irrigation modernization and water conservation in Spain: The case of Riegos del Alto Aragón

This study analyzes the effects of irrigation modernization on water conservation, using the Riegos del Alto Aragón (RAA) irrigation project (NE Spain, 123354 ha) as a case study. A conceptual approach, based on water accounting and water productivity, has been used. Traditional surface irrigation systems and modern sprinkler systems currently occupy 73% and 27% of the irrigated area, respectively. Virtually all the irrigated area is devoted to field crops. Nowadays, farmers are investing on irrigation modernization by switching from surface to sprinkler irrigation because of the lack of labour and the reduction of net incomes as a consequence of reduction in European subsidies, among other factors. At the RAA project, modern sprinkler systems present higher crop yields and more intense cropping patterns than traditional surface irrigation systems. Crop evapotranspiration and non-beneficial evapotranspiration (mainly wind drift and evaporation loses, WDEL) per unit area are higher in sprinkler irrigated than in surface irrigated areas. Our results indicate that irrigation modernization will increase water depletion and water use. Farmers will achieve higher productivity and better working conditions. Likewise, the expected decreases in RAA irrigation return flows will lead to improvements in the quality of the receiving water bodies. However, water productivity computed over water depletion will not vary with irrigation modernization due to the typical linear relationship between yield and evapotranspiration and to the effect of WDEL on the regional water balance. Future variations in crop and energy prices might change the conclusions on economic productivity.     

Building a climate resilient farm: A risk based approach for understanding water, energy and emissions in irrigated agriculture

The links between water application, energy consumption and emissions are complex in irrigated agriculture. There is a need to ensure that water and energy use is closely considered in future industry planning and development to provide practical options for adaptation and to build resilience at the farm level. There is currently limited data available regarding the uncertainty and sensitivity associated with water application and energy consumption in irrigated crop production in Australia. This paper examines water application and energy consumption relationships for different irrigation systems, and the ways in which the uncertainty of different parameters impacts on these relationships and associated emissions for actual farms. This analysis was undertaken by examining the current water and energy patterns of crop production at actual farms in two irrigated areas of Australia (one using surface water and the other groundwater), and then modelling the risk/uncertainty and sensitivity associated with the link between water and energy consumption at the farm scale. Results showed that conversions from gravity to pressurised irrigation methods reduced water application, but there was a simultaneous increase in energy consumption in surface irrigation areas. In groundwater irrigated areas, the opposite is true; the use of pressurised irrigation methods can reduce water application and energy consumption by enhancing water use efficiency. Risk and uncertainty analysis quantified the range of water and energy use that might be expected for a given irrigation method for each farm. Sensitivity analysis revealed the contribution of climatic (evapotranspiration and rainfall) and technical factors (irrigation system efficiency, pump efficiency, suction and discharge head) impacting the uncertainty and the model output and water-energy system performance in general. Flood irrigation systems were generally associated with greater uncertainty than pressurised systems. To enhance resilience at the farm level, the optimum situation envisaged an irrigation system that minimises water and energy consumption and greenhouse gas emissions. Where surface water is used, well designed and managed flood irrigation systems will minimise the operating energy and carbon equivalent emissions. Where groundwater is the dominant use, the optimum system is a well designed and managed pressurised system operating at the lowest discharge pressure possible that will still allow for efficient irrigation. The findings might be useful for farm level risk mitigation strategies in surface and groundwater systems, and for aiding adaptation to climate change.     

Forecasting and optimizing furrow irrigation management decision variables

Furrow irrigation can be better managed if the management decision variables (irrigation time and amount; inflow rate and cutoff) can be determined ahead of time. In this study, these decision variables were forecast and optimized using 1 day ahead grass reference crop evapotranspiration (ET₀) forecasts, based on the ARMA (1,1) time-series model, with a seasonal furrow irrigation model for both homogeneous and heterogeneous infiltration conditions. Heterogeneity in infiltration characteristics was restricted to variations along the furrow length as opposed to variations between furrows. The results obtained were compared with their counterparts using the observed ET₀ for the same period during the 1992 cropping season. Seasonal performance (application efficiency, inflow, runoff and deep percolation volumes) and economic return to water (yield benefits minus seasonal water related and labor costs) were affected by infiltration conditions, while irrigation requirement and bean yield were unchanged. In a given infiltration case, seasonal performance, irrigation schedules, bean yield and economic return to water were comparable (lower than 4% difference) for the two ET₀ conditions. For each ET₀ condition, individual irrigation events resulted in different irrigation designs (inflow rate and cutoff time) except inflow rates with heterogeneous infiltration. Differences in inflow volume were less than 2% and 5%, respectively, for homogeneous infiltration and heterogeneous infiltration. For the conditions studied, furrow irrigation management decision variables can be forecast and optimized to better manage the irrigation system, because irrigation performance was the same for both (forecast and observed) ET₀ cases. Received: 9 October 1999     

Surface energy balance using satellite data for the water balance of a traditional irrigation—wetland system in SW Iran

A water balance of a large traditional irrigation area and a downstream adjoining wetland was determined using the surface energy balance approach (SEB), based on satellite data, to calculate the actual evaporation of both the irrigated area and the wetland at four different dates in a dry year and information of two additional images. The contribution of capillary flow by the shallow groundwater table was estimated by evaluating the actual evapotranspiration values of adjoining rangelands and non-inundated wetland areas. Those values were used to separate the total evapotranspiration into a soil moisture change component due to capillary rise, and into a component attributable to supply of river water. The only field data used for the estimated monthly water balance were air temperature, wind speed, and water inflow, since rainfall and outflow could be ignored in the year 2000. The results provided an insight for conditions of a drought year within the irrigated area, the distribution of water to irrigation and the wetland and showed the linkage between inundated wetland area and discharge.     

Integrated decision making for reservoir,irrigation, and crop management

An integrated approach to reservoir, irrigation, and cropping management which links four different models—a hydrologic model (PRMS), a crop growth simulation model (EPIC), an economic model based on linear programming, and a dynamic programming model—is developed and demonstrated. The demonstration is based on an irrigation district located in a subhumid climate with an irrigation reservoir large enough for over-year storage. The model is used to make repeated simulations for various planning horizons. Two different types of results are presented. The first provides the probability that each of the various farm plans (land/crop/water allocation) will be chosen as the optimum in the first year of the planning horizon. The second approach provides probability distributions of accumulated revenues over a chosen length of planning horizon. Each distribution is associated with an initial reservoir level and a particular farm plan in the first year of the planning horizon. The consequence of selecting certain farm plans at the beginning of a specified planning horizon is therefore quantified in a probabilistic way. Based on families of probability–revenue curves, an irrigation manager can simultaneously evaluate crop, irrigation, and reservoir management options.     

基于DP-PSO算法的灌区农业水资源优化配置

针对引水灌区,考虑灌溉用水总量约束和时段可供水量约束,以单一作物非充分灌溉下灌溉制度优化为第一层,区域多种作物种植结构及水量分配为第二层,分别采用动态规划和粒子群算法求解,建立了基于DP-PSO算法的灌区农业水资源优化配置模型。针对多重约束问题,提出了一种初始化粒子群的方法,并通过粒子速度的动态变化来保证每代粒子都满足约束,提高了算法的收敛速度和收敛精度。以赣抚平原灌区为对象,考虑降雨和水源可供水量不同步的特点,计算了3种降雨频率、多年可供水条件下的农业水资源优化配置方案。结果表明,基于DPPSO算法的农业水资源优化配置模型合理可靠,为引水灌区农业水资源优化提供了一种新的方法。     

Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing

As water resources are limited and the demand for agricultural products increases, it becomes increasingly important to use irrigation water optimally. At a farm scale, farmer's have a particularly strong incentive to optimize their irrigation water use when the volume of water available over a season is production limiting. In this situation, a farmer's goal is to maximize farm profit, by adjusting when and where irrigation water is used. However, making the very best decisions about when and where to irrigate is not easy, since these daily decisions require consideration of the entire remaining irrigation season. Future rainfall uncertainty further complicates decisions on when and which crops should be subjected to water stress. This paper presents an innovative on-farm irrigation scheduling decision support method called the Canterbury irrigation scheduler (CIS) that is suitable when seasonal water availability is limited. Previous optimal scheduling methods generally use stochastic dynamic programming, which requires over-simplistic plant models, limiting their practical usefulness. The CIS method improves on previous methods because it accommodates realistic plant models. Future farm profit (the objective function) is calculated using a time-series simulation model of the farm. Different irrigation management strategies are tested using the farm simulation model. The irrigation strategies are defined by a set of decision variables, and the decision variables are optimized using simulated annealing. The result of this optimization is an irrigation strategy that maximizes the expected future farm profit. This process is repeated several times during the irrigation season using the CIS method, and the optimal irrigation strategy is modified and improved using updated climate and soil moisture information. The ability of the CIS method to produce near optimal decisions was demonstrated by a comparison to previous stochastic dynamic programming schedulers. A second case study shows the CIS method can incorporate more realistic farm models than is possible when using stochastic dynamic programming. This case study used the FarmWi$e/APSIM model developed by CSIRO, Australia. Results show that when seasonal water limit is the primary constraint on water availability, the CIS could increase pasture yield revenue in Canterbury (New Zealand) in the order of 10%, compared with scheduling irrigation using current state of the art scheduling practice.     

A new stochastic optimization model for deficit irrigation

Deficit irrigation has been suggested as a way to increase system benefits, at the cost of individual benefits, by decreasing the crop water allocation and increasing the total irrigated land. Deterministic methods are common for determining optimal irrigation schedules with deficit irrigation because considering the inherent uncertainty in crop water demands while including the lower and upper bounds on soil moisture availability is a hard problem. To deal with this, a constraint state formulation for stochastic control of the weekly deficit irrigation strategy is proposed. This stochastic formulation is based on the first and second moment analysis of the stochastic soil moisture state variable, considering soil moisture as bounded between a maximum value and a minimum value. As a result, an optimal deficit irrigation scheduling is determined using this explicit stochastic model that does not require discretization of system variables. According to the results, if irrigation strategy is based on deterministic predictions, achievement of high, long-term expected relative net benefits by decreased crop water allocation and increased irrigated land may have a higher failure probability.     

基于遥感的农业用水效率评价方法研究进展

遥感技术的发展为区域尺度蒸散发计算、作物分布识别及估产提供了一条有效途径,为基于遥感信息的灌区灌溉水利用效率及作物水分利用效率定量评价奠定了基础。回顾总结了遥感蒸散发模型、瞬时蒸散发升尺度方法、日蒸散发插值方法、作物分布识别方法及作物估产模型的研究进展,评述了遥感蒸散发及作物估产结果在灌区灌溉水利用效率及作物水分利用效率评价中的应用情况。提出了相关领域需要进一步研究的问题,包括适合非均匀下垫面特点且具有较强物理基础的灌区遥感蒸散发模型、日蒸散发插值中灌溉或降雨引起土壤含水量突变情况的处理、农田蒸散发中灌溉水有效消耗量的准确估算、能适应复杂种植结构并且适用于多年的作物分布遥感识别模型以及精度较高且可操作性强的遥感估产模型等。     

A fuzzy risk approach for performance evaluation of an irrigation reservoir system

In this paper, a model for fuzzy risk of low yield of a crop is developed to study the implications of a reservoir operating policy model. When an optimal operating policy is derived based on a known objective, the policy itself does not, in general, indicate a measure of the system performance unless a criterion to this effect is embedded in the objective function. While a systems analyst is interested in the nature of the objective function used in arriving at a policy, the irrigation decision maker would look for the implications of using the policy through answers to the questions such as, how often the system will fail and how quickly it will recover from a failure. It is, therefore, important that the implications of reservoir operation with a given policy be studied keeping in view the interests of the decision makers. Some earlier studies on reservoir operation models for irrigation have considered reliability, resiliency and productivity index, as the performance indicators of the operating policy. In this paper, fuzzy risk of low yield of a crop is considered as another performance indicator to address uncertainties due to both randomness and fuzziness. Uncertainty due to randomness arises primarily because of the random variations of hydrologic variables such as reservoir inflows and rainfall in the command area. Uncertainty due to imprecision or fuzziness arises because of uncertain crop yield response to various factors (such as farm practices and climatic variables) other than to the applied water. Two important concepts are introduced in this paper with respect to irrigation reservoir system. The first one is related to viewing the low yield of a crop, as a fuzzy event. The second concept is related to the definition of fuzzy risk of low yield of a crop. The fuzzy risk of low yield is derived using the concept of probability of a fuzzy event. Application of the methodology is demonstrated with a case study in India.     

Regulated deficit irrigation during the kernel-filling period and optimal irrigation rates in almond

The use of Regulated deficit irrigation (RDI) in almond, applied during the kernel-filling phase, was evaluated over four consecutive years. To determine the reference optimal irrigation rate, three treatments were applied: T-100, which was irrigated by replacing crop evapotranspiration; T-130, which was irrigated by applying 30% more water than in T-100 and T-70, which received 30% less water than T-100. The RDI treatment received the same irrigation rate as T-100, but during the kernel-filling period irrigation was reduced to 20% of T-100. The optimum yield response was observed in treatment T-100, while T-130 trees never improved on T-100 kernel production over the 4 years of the study. During the first two experimental years, kernel dry matter accumulation did not decrease with drought in the RDI treatment. However, both cropping and kernel growth were reduced during the third and fourth years of the experiment. A possible explanation for this decrease could be found in a hypothetical depletion of the carbohydrate reservoir in RDI trees and also to the negative soil water balance that was evident in the T-70 and RDI treatments during winter and spring of the last 2 years. Although yield reductions for RDI trees were significant (20% with respect to T-100), the water savings obtained (about 60% of that applied with respect to T-100), may help to promote the adoption of RDI in areas, where water availability has been reduced. Bearing in mind the water conservation aspect in almond, RDI, as applied in this case, seemed more interesting than a seasonal sustained deficit irrigation strategy like T-70.     

Using the depleted fraction to manage the groundwater table in irrigated areas

The depleted fraction, defined as the ratio of ET_actual over total inflow (P + V_c), relates parameters of the water balance of an irrigated area with each other in such a way that the (water) manager obtains information on the rate of change of water stored in the area (soil moisture and groundwater). If the annual average of the depleted fraction equals about 0.6 water storage in the area is stable, while water is stored for lower values of the depleted fraction. If the value of the depleted fraction exceeds about 0.6, the volume of water stored in the area decreases. This decrease is partly due to natural drainage and partly due to capillary rise into the root zone of the irrigated crop. Despite this capillary rise, the actual evapo-transpiration drops below the potential ET-value. For most crops, a decrease of ET by about 25% would result in a higher productivity in terms of yield per cubic meter water. However, the yield per hectare (and thus farm income) would decrease. Management of an irrigation system is recommended in such a way that the monthly values of the depleted fraction range is between 0.5 and 0.8. Such a management rule would provide sufficient water for leaching (at the 0.5 side of the range) and provide high crop yield per unit water consumed (at the 0.8 side).     

Assessing the impact of the Nitrate Directive on farming systems using a bio-economic modelling chain

Bio-economic models can be used to assess the impact of policy and environmental measures through economic and environmental indicators. Focusing on agricultural systems, farmers’ decisions in terms of cropping systems and the associated crop management at field scale are essential in such studies. The objective of this paper is to present a study using a bio-economic model to assess the impact of the Nitrate Directive in the Midi-Pyrenees region (France) by analyzing, at the farm scale, farm income and three environmental indicators: nitrate leaching, erosion and water consumption. Two scenarios, the 2003 CAP reform (baseline scenario) and the Nitrate Directive (policy scenario), with a 2013 time horizon, were developed and compared for three representative arable farm types in the Midi-Pyrenees region. Different types of data characterizing the biophysical context in the region (soil, climate), the current cropping systems (rotation, crop management) and farm resources (irrigated land, labor) were collected to calibrate and run the models. Results showed that the implementation of the Nitrate Directive may not affect farm income. However, significant modifications to cropping systems and crop allocation to soil types were simulated. Contrary to expectations, nitrogen leaching at the farm scale did not change. Overall water consumption increased and soil erosion decreased due mainly to a modification in cropping patterns and management by soil type. This study provides an example of unanticipated effects of policy and trade-offs between environmental issues.     

Evolving strategies for crop planning and operation of irrigation reservoir system using multi-objective differential evolution

In this paper multi-objective differential evolution (MODE) approach is proposed for the simultaneous evolution of optimal cropping pattern and operation policies for a multi-crop irrigation reservoir system. In general, farming community wants to maximize total net benefits by irrigating high economic value crops over larger area, which may also include water-intensive crops and longer duration crops. This poses a serious problem under water-scarce conditions and often results in crop failure. Under varying hydrological conditions, the fixed cropping pattern with conventional operating rule curve policies may not yield economically good results. To provide flexible policies, a nonlinear multi-objective optimization model is formulated. To achieve robust performance by handling interdependent relationships among the decision variables of the model, the recent MODE technique is adopted to solve the multi-objective problem. The developed model is applied for ten-daily reservoir operation to a case study in India. The model results suggest that changes in the hydrologic conditions over a season have considerable impact on the cropping pattern and net benefits from the irrigation system. Towards this purpose, the proposed MODE model can be used to evolve different strategies for irrigation planning and reservoir operation policies, and to select the best possible solution appropriate to the forecasted hydrologic condition.     

Impact of crop rotation and minimum tillage on water use efficiency of irrigated cotton in a Vertisol

Crop water use efficiency of irrigated cotton was hypothesized to be improved by a combination of minimum tillage and sowing a wheat (Triticum aestivum L.) rotation crop. This hypothesis was evaluated in a Vertisol near Narrabri, Australia from 1997 to 2003. The experimental treatments were: continuous cotton sown after conventional or minimum tillage and minimum-tilled cotton–wheat. Soil water content was measured with a neutron moisture meter, and runoff with trapezoidal flumes. Application efficiency of irrigation water was estimated as the amount of infiltrated water/total amount applied. Plant available water was estimated using the maximum and minimum soil water storage during the growing season. Evapotranspiration was estimated with the water balance method using measured and simulated soil water data. Seasonal evapotranspiration was partitioned into that coming from rainfall, irrigation and stored soil water. Crop water use efficiency was calculated as cotton lint yield per hectare/seasonal evapotranspiration. Rotation of cotton with wheat and minimum tillage improved water use efficiency in some years and application efficiency in all years. Average seasonal evapotranspiration was higher with minimum tillage than with conventional tillage. In years when cotton was sown in all plots, average cotton crop water use efficiencies were 0.23, 0.23 and 0.22 kg (lint)/m³ for minimum-tilled cotton–wheat and continuous cotton, and conventionally tilled continuous cotton, respectively. In-season rainfall efficiency, transpiration and soil evaporation were unaffected by cropping system.