Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture

TLDR: In this paper, the authors presented an approach to explore water management options in irrigated agriculture considering the constraints of water availability and the heterogeneity of irrigation system properties using remote sensing (RS) data.

About: This article is published in Agricultural Water Management.The article was published on 2006-06-16 and is currently open access. It has received 110 citations till now. The article focuses on the topics: Deficit irrigation & Evapotranspiration.

Figures

Table 2 – Best management options derived by genetic algorithma

Fig. 4 – Solution of the water management optimization model when average seasonal water available in the system, QaveS = 200 mm: (a) evolution of maximum fitness and (b) average yield and irrigation as a function of number of generations.

Fig. 1 – Location of the study area in the Bhakra Irrigation

Fig. 5 – (a) Crop yields at different levels of water availability ge Distributions of yields and water productivity relative to irrigatio with optimization (N = 250 realizations) for 2000–2001 rabi (dry)

Fig. 2 – (a) Modeling framework used in the study: (i) system characterization (bold dashed line enclosure), and (ii) exploring water management options (thin dashed line enclosure). (b) Schematic of the stochastic data assimilation technique where the distributions of ET calculated by SEBAL are matched with SWAP simulations using genetic algorithm.

Fig. 6 – (a) Water productivity relative to: irrigation applied, PWIrr the best management practices; (b) evapotranspiration, PWDeple the best management practices; and (c) crop transpiration, PWPr the best management practices. (Legend: (*) outliers, (*) extrem

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture" ?

The authors present an innovative approach to explore water management options in irrigated agriculture considering the constraints of water availability and the heterogeneity of irrigation system properties. Their results showed that under limited water condition, regional wheat yield could improve further if water and crop management practices are considered simultaneously and not independently. 

Q2. Why does wheat produce some yields when water is very limited?

Due to the effect of antecedent soil moisture and rainfall in the growing season (here, 91 mm), crops still could produce some yields when water is very limited. 

Q3. What is the principle of the distribution of water?

The distribution of water is based on thewarabandi principle, which is a supply driven fixed-time rotational water delivery system (Berkoff and Huppert, 1987). 

Q4. What is the purpose of this paper?

The purpose of this paper is to explore water management options in irrigated agriculture by using a combined RSsimulation modeling and genetic algorithm optimization. 

Q5. What is the effect of the reduced irrigation on wheat ET?

The sink of wheat ET during the rabi (dry) season caused by reduced irrigation applications will be replenished by rainfall during the monsoon period. 

Q6. What is the importance of the analysis of the crop level?

Crop level analysis is important in this case, and upscaling the procedure to the system level is equally important to account for the impacts of system heterogeneities (Ines et al., 2002). 

Q7. What could be the reason for the wide distribution of sowing dates?

A possible explanation could be, the wider distribution of sowing dates may give some farmers greater degrees of freedom to use more water than the others. 

Q8. What is the definition of a paradigm shift in water management?

Under this circumstance a paradigm shift appears to be necessary, from a demand driven water management into a more supply driven one. 

Q9. Why was the limited number of generations used to minimize the computational time?

The limited number of generations was deliberately used to minimize the computational time because every chromosome (k) in a generation is resampled 250 times to capture the spatial behaviour of the system (Table 2). 

Q10. What is the way to improve the performance of a warabandi system?

In their perspective, it appears that a rotational water delivery scheme at the minor (lateral) level could improve further the performance of a warabandi system. 

Q11. What is the probability density function of each variable in a chromosome?

the probability density function of each variable in a chromosome is resampled to generate a combination of deviates that would represent a homogenous soil unit to be simulated by SWAP. 

Q12. What are the limiting factors that dominate crop growth when water is very limited?

This is likely to be explained by the limiting factors that dominate crop growth when water applied is very limited or in abundant supply, which are salinity, water and oxygen stress.