Modeling soil water balance and irrigation strategies in a flood-irrigated wheat-maize rotation system. A case in dry climate, China

Zhou, Hong; Zhao, Wenlong

doi:10.1016/j.agwat.2019.05.011

Cited by 33 publications

(17 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The historical evolution of these models was described by [6][7][8]. Nevertheless, modeling of irrigation systems has traditionally been limited to soil dynamics, neglecting the plants' interactions and effects [9], which has led to sub-optimal solutions [10]. Consequently, the design of irrigation systems can be enhanced by more comprehensive models that take interactions between the different system compartments into account and by a more systematic use of system and control theory to improve monitoring and predictive control.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

Lopez-Jimenez

Wouwer

Quijano

2022

Water

View full text Add to dashboard Cite

The smart use of water is a key factor in increasing food production. Over the years, irrigation has relied on historical data and traditional management policies. Control techniques have been exploited to build automatic irrigation systems based on climatic records and weather forecasts. However, climate change and new sources of information motivate better irrigation strategies that might take advantage of the new sources of information in the spectrum of systems and control methodologies in a more systematic way. In this connection, two open questions deserve interest: (i) How can one deal with the space–time variability of soil conditions? (ii) How can one provide robustness to an irrigation system under unexpected environmental change? In this review, the different elements of an automatic control system are described, including the mathematical modeling of the crop–soil systems, instrumentation and actuation, model identification and validation from experimental data, estimation of non-measured variables and sensor fusion, and predictive control based on crop–soil and weather models. An overview of the literature is given, and several specific examples are worked out for illustration purposes.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

Lopez-Jimenez

Wouwer

Quijano

2022

Water

View full text Add to dashboard Cite

show abstract

“…Previous studies have focused on ways to reduce N loss by regulating hydrological processes with crop mulching [19], addition of biochar and organic fertilization [16,20], intercropping [21], and the optimization of irrigation schedule and fertilizer applications [14,22]. However, the implementation of those measures is limited in Zhangye irrigation district.…”

Section: Introductionmentioning

confidence: 99%

Fine Soil Texture Is Conducive to Crop Productivity and Nitrogen Retention in Irrigated Cropland in a Desert-Oasis Ecotone, Northwest China

Wang

Zhao

et al. 2022

Agronomy

View full text Add to dashboard Cite

Agricultural production in oases requires extensive irrigation and nitrogen (N) inputs, which result in a high incidence of non-point-source pollution. Information on how soil texture affects crop productivity and water and N use efficiency is needed to improve N management in oases. A two-year field study with six free-draining leaching pits was set up to quantify soil water content (SWC), drainage, yield, N uptake, soil residual N, N leaching, water-use efficiency (WUE), and N fertilizer-use efficiency (NFUE) on sandy-textured soils in a young oasis field (24 years, YOF) and loam-textured soils in an old oasis field (>60 years; OOF) within an intensive oasis agricultural zone in Northwest China. The results showed that sand content in the YOF was significantly higher than that in the OOF (p < 0.01), while both clay and silt contents in the YOF were significantly lower than those in the OOF (p < 0.01). Soil water storage (SW) in the 0–100 cm soil layer, evapotranspiration water-use efficiency (WUEET) and irrigation water-use efficiency (WUEIrrig) in the OOF were, respectively, 87.1, 23.5, and 24.1% higher than those in the YOF (p < 0.05), and deep drainage in the YOF was 6.2 times higher than that in the OOF (p < 0.01). Maize N uptake and soil N accumulation in the OOF in the 0–100 cm soil layer were, respectively, 33.4 and 10.3% higher than those in the YOF (p < 0.05), and N leaching loss in the YOF was 1.65 times higher than that in the OOF (p > 0.01). Crop yield and NFUE in the OOF were, respectively, 23.9 and 27.2% higher than those in the YOF (p < 0.05). These results indicated that sandy-textured soils with high sand content were not conducive to water and N retention, resulting in lower crop yields and water- and fertilizer-use efficiency in YOF. Therefore, it is imperative that management practices in sandy-textured land promote improvements in soil structure and maintain the long-term productivity of the young cultivated fields.

show abstract

“…A clear understanding of water movement and redistribution would improve soil water‐holding capacity and farmland water use efficiency and further benefit dryland farming (Liang, et al., 2020; Liu et al., 2020; Wang et al., 2020). The soil water retention curve (SWRC) represents the soil's ability to retain water under certain soil water matric potential, which is also one of main input parameters required for simulation of soil water movement and solute transport (Fan, Baumgartl, Scheuermann, & Lockington, 2015; Zhou & Zhao, 2019). Zhai and Rahardjo (2015) demonstrated that the estimation of the permeability function from the SWRC was more accurate when using the degree of saturation (considering soil volume change) than using the gravimetric soil moisture (ignoring soil volume change).…”

Section: Introductionmentioning

confidence: 99%

A novel method to inverse the water retention curves with consideration of volume change during centrifuge testing

Lai

Garg

Chang

et al. 2021

Soil Science Soc of Amer J

View full text Add to dashboard Cite

Measuring the soil water retention curve (SWRC) using the centrifuge method often results in compressibility and an increase in bulk density. The compressibility and bulk density of the specimen are always assumed to be constant while deducing the SWRC from centrifuge testing, which may cause inaccurate SWRCs. This study proposed an improved genetic algorithm (GA) to inverse the hydraulic parameters and further constructed the SWRC by taking bulk density into account for soils with varying grain size compositions. For this study, sandy loam soils (i.e., seven treatments T1–T7) were prepared based on different volume ratios of coarse (0.075–2 mm) to fine (<0.075 mm) particles. SWRCs including compressibility and bulk density changes were measured using the centrifuge method. For verification, SWRCs were also independently deduced from one‐dimensional infiltration experiments by inverse analysis using the improved GA that combined multi‐objective optimization and elitist strategy. Upon the final shrinkage ratio of 18.32 to ∼26.38%, the bulk density during centrifugation increased by 22.33 to ∼35.72% correspondingly, thereby causing a difference in soil moisture of 9.58 to ∼16.53% between the corrected and uncorrected SWRCs. The comparison between simulated SWRC and two types of measured SWRCs (i.e., corrected and uncorrected SWRC) demonstrated that the corrected SWRC was more reasonable regardless of soil particle compositions. The findings suggest the corrected SWRC considering bulk density changes provides more accurate hydraulic parameters, which can be inversed from the infiltration data using the improved GA.

show abstract

Modeling soil water balance and irrigation strategies in a flood-irrigated wheat-maize rotation system. A case in dry climate, China

Cited by 33 publications

References 67 publications

Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

Fine Soil Texture Is Conducive to Crop Productivity and Nitrogen Retention in Irrigated Cropland in a Desert-Oasis Ecotone, Northwest China

A novel method to inverse the water retention curves with consideration of volume change during centrifuge testing

Contact Info

Product

Resources

About