Special Issue "Optimization of Water Use in Agricultural Systems"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water, Agriculture and Aquaculture".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 2876

Special Issue Editor

Dr. Saseendran S. Anapalli
E-Mail Website
Guest Editor
Sustainable Water Management Research Unit, USDA-Agricultural Research Service, Ft Collins, CO 80526, USA
Interests: agriculture; limited irrigation water management; plant physiology; sustainable agriculture; water resources management; climate change; agricultural system modeling; climate change impacts in agriculture; eddy covariance flux monitoring
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Special Issue Information

Dear Colleagues,

Fresh water, especially ground water, available for supporting crop production across the world is declining quickly due to competing demands for this commodity from multiple burgeoning human enterprises. The need for more and more food production for the ever increasing human and animal population calls for the development of measures to make irrigated agriculture more sustainable by developing better crop-lines and agro-management practices that produce higher yields with less water. Optimizing crop water use through dedicated research for increased water use efficiency in crop production is key for assuring food security for everyone in the world. This endeavor calls for collecting and publishing world-wide cutting-edge research about optimizing water use in agriculture.

Dr. Saseendran S. Anapalli
Guest Editor

Manuscript Submission Information

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Keywords

  • crop water
  • water use efficiency
  • agricultural optimization
  • irrigation management
  • crop production
  • eddy covariance
  • evapotranspiration
  • climate change

Published Papers (3 papers)

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Research

Article
Simple and Two-Level Hierarchical Bayesian Approaches for Parameter Estimation with One- and Two-Layer Evapotranspiration Models of Crop Fields
Water 2021, 13(24), 3607; https://doi.org/10.3390/w13243607 - 15 Dec 2021
Viewed by 672
Abstract
The two-layer Shuttleworth–Wallace (SW) evapotranspiration (ET) model has been widely used for predicting ET with good results. Since the SW model has a large number of specific parameters, these parameters have been estimated using a simple non-hierarchical Bayesian (SB) approach. To [...] Read more.
The two-layer Shuttleworth–Wallace (SW) evapotranspiration (ET) model has been widely used for predicting ET with good results. Since the SW model has a large number of specific parameters, these parameters have been estimated using a simple non-hierarchical Bayesian (SB) approach. To further improve the performance of the SW model, we aimed to assess parameter estimation using a two-level hierarchical Bayesian (HB) approach that takes into account the variation in observed conditions through the comparison with a traditional one-layer Penman–Monteith (PM) model. The difference between the SB and HB approaches were evaluated using a field-based ET dataset collected from five agricultural fields over three seasons in Myanmar. For a calibration period with large variation in environmental factors, the models with parameters calibrated by the HB approach showed better fitting to observed ET than that with parameters estimated using the SB approach, indicating the potential importance of accounting for seasonal fluctuations and variation in crop growth stages. The validation of parameter estimation showed that the ET estimation of the SW model with calibrated parameters was superior to that of the PM model, and the SW model provided acceptable estimations of ET, with little difference between the SB and HB approaches. Full article
(This article belongs to the Special Issue Optimization of Water Use in Agricultural Systems)
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Article
Spatiotemporal Variation of Water Supply and Demand Balance under Drought Risk and Its Relationship with Maize Yield: A Case Study in Midwestern Jilin Province, China
Water 2021, 13(18), 2490; https://doi.org/10.3390/w13182490 - 10 Sep 2021
Cited by 3 | Viewed by 801
Abstract
Under the background of global warming, the frequent occurrence and long-term persistence of drought events have substantial negative effects on agricultural production. As the main maize production area in midwestern Jilin Province, frequent drought and a shortage of irrigation water pose substantial threats [...] Read more.
Under the background of global warming, the frequent occurrence and long-term persistence of drought events have substantial negative effects on agricultural production. As the main maize production area in midwestern Jilin Province, frequent drought and a shortage of irrigation water pose substantial threats to the production of maize. We analyzed the balance of water supply and demand in each growth period and the degree of maize yield affected by drought. The results indicate that the FIO-ESM climate model can effectively simulate the changes in temperature and precipitation, and was highly applicable to the study area. From 1980 to 2020, the drought risk indices for the sowing to jointing, jointing to tasseling, tasseling to milk-ripe, and milk-ripe to maturity stages were 0.62, 0.52, 0.48, and 0.60, respectively. In the future, the chances of a RCP8.5 scenario drought risk and an enhanced RCP4.5 scenario have eased. Spatially, the high-risk areas shift in a “west−central−southwest” pattern. Effective precipitation will decrease in the future, while the increasing water requirement of maize increases the dependence on irrigation water. The irrigation requirement index is more than 70% for all periods, particularly in the milk-ripe to maturity stage. The relative meteorological yields were positively correlated with the CWDI of the whole growth period, with the rate of reduction in maize yield and the yield reduction coefficient of variation at a high level of risk between 1980 and 2020. In the future, the negative impact of drought risk on the yield of maize lessened with no obvious trend in production. In particular, the rate of reduction and reduction coefficient of variation for the RCP8.5 scenario were 1.24 and 1.09, respectively. Full article
(This article belongs to the Special Issue Optimization of Water Use in Agricultural Systems)
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Article
Water Use Efficiencies of Different Maturity Group Soybean Cultivars in the Humid Mississippi Delta
Water 2021, 13(11), 1496; https://doi.org/10.3390/w13111496 - 27 May 2021
Cited by 2 | Viewed by 901
Abstract
Introducing alternative cultivars with enhanced water use efficiencies can help alleviate pressure on groundwater for crop irrigations in Mississippi (MS) Delta. A two-year field study was conducted in 2019–2020 to compare the water use efficiencies (WUE) of recently released and pre-released soybean { [...] Read more.
Introducing alternative cultivars with enhanced water use efficiencies can help alleviate pressure on groundwater for crop irrigations in Mississippi (MS) Delta. A two-year field study was conducted in 2019–2020 to compare the water use efficiencies (WUE) of recently released and pre-released soybean {Glycine max (L.) Merr.} cultivars in maturity group (MG) III (‘P37A78’, ‘LG03-4561-14’), IV (‘Dyna-gro 4516x’, ‘DS25-1, DT97-4290’), and V (‘S12-1362’, ‘S14-16306’) in the MS Delta. The experimental design was a split-plot with cultivar as the first factor and the second factor was water variant irrigation (IR) and no irrigation (RF, rainfed), replicated three times. The MG IV cultivar Dyna-gro 4516x recorded the highest grain yield and WUE: grain yields were 4.58 Mg ha−1 and 3.89 Mg ha−1 under IR and RF, respectively in 2019, and 4.74 Mg ha−1 and 4.35 Mg ha−1 in 2020. The WUE were 7.2 and 6.9 kg ha−1 mm−1, respectively, in 2019 under IR and RF, and 13.4 and 16.9 kg ha−1 mm−1 in 2020. The data reveals that ‘Dyna-gro 4516x’ (MG IV), ‘LG03-4561-14’ (MG III), and ‘P37A78’ (MG III) are best adapted to the early soybean production system (ESPS) in MS Delta region for sustainable production for conserving water resources. Full article
(This article belongs to the Special Issue Optimization of Water Use in Agricultural Systems)
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