Degradation of agricultural drainage water quantity and quality due to farmland expansion and water-saving operations in arid basins

Hu, Qiuli; Yang, Yonghui; Han, Shijie; Wang, Jiusheng

doi:10.1016/j.agwat.2018.10.019

Cited by 35 publications

(14 citation statements)

References 32 publications

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“…In this study, drainage capacity, irrigation capacity, ease of farming, and traffic accessibility were chosen as socioeconomic indicators. Water resource is an important factor influencing crop yields [70,71]. A proper drainage and irrigation pattern not only improves drainage capacity, but also maintains the soil moisture of the farmland [72].…”

Section: Superiority Testmentioning

confidence: 99%

Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Wang

Yong

et al. 2021

Agriculture

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Constructing a scientific and quantitative quality-assessment model for farmland is important for understanding farmland quality, and can provide a theoretical basis and technical support for formulating rational and effective management policies and realizing the sustainable use of farmland resources. To more accurately reflect the systematic, complex, and differential characteristics of farmland quality, this study aimed to explore an intelligent farmland quality-assessment method that avoids the subjectivity of determining indicator weights while improving assessment accuracy. Taking Xiangzhou in Hubei Province, China, as the study area, 14 indicators were selected from four dimensions—terrain, soil conditions, socioeconomics, and ecological environment—to build a comprehensive assessment index system for farmland quality applicable to the region. A total of 1590 representative samples in Xiangzhou were selected, of which 1110 were used as training samples, 320 as test samples, and 160 as validation samples. Three models of entropy weight (EW), backpropagation neural network (BPNN), and random forest (RF) were selected for training, and the assessment results of farmland quality were output through simulations to compare their assessment accuracy and analyze the distribution pattern of farmland quality grades in Xiangzhou in 2018. The results showed the following: (1) The RF model for farmland quality assessment required fewer parameters, and could simulate the complex relationships between indicators more accurately and analyze each indicator’s contribution to farmland quality scientifically. (2) In terms of the average quality index of farmland, RF > BPNN > EW. The spatial patterns of the quality index from RF and BPNN were similar, and both were significantly different from EW. (3) In terms of the assessment results and precision characterization indicators, the assessment results of RF were more in line with realities of natural and socioeconomic development, with higher applicability and reliability. (4) Compared to BPNN and EW, RF had a higher data mining ability and training accuracy, and its assessment result was the best. The coefficient of determination (R2) was 0.8145, the mean absolute error (MAE) was 0.009, and the mean squared error (MSE) was 0.012. (5) The overall quality of farmland in Xiangzhou was higher, with a larger area of second- and third-grade farmland, accounting for 54.63%, and the grade basically conformed to the trend of positive distribution, showing an obvious pattern of geographical distribution, with overall high performance in the north-central part and low in the south. The distribution of farmland quality grades also varied widely among regions. This showed that RF was more suitable for the quality assessment of farmland with complex nonlinear characteristics. This study enriches and improves the index system and methodological research of farmland quality assessment at the county scale, and provides a basis for achieving a threefold production pattern of farmland quantity, quality, and ecology in Xiangzhou, while also serving as a reference for similar regions and countries.

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Section: Superiority Testmentioning

confidence: 99%

Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Wang

Yong

et al. 2021

Agriculture

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“…Os níveis de sais e nutrientes são impulsionados principalmente pela quantidade e qualidade da água lixiviada de origem agrícola, e trabalhos têm indicado a correlação da drenagem agrícola no processo de degradação da qualidade, da mesma forma que pode limitar a disponibilidade de água em regiões a jusante (VAN VLIET et al, 2017;HU et al, 2019).…”

Section: Classificação Das áGuas Para Fins De Irrigaçãounclassified

Variabilidade espaço-temporal da qualidade da água em área de agricultura irrigada

Sales

Netto

Monteiro

et al. 2020

RBAI

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“…Wang et al (2018) used a water-balance model to assess the effects of water-saving irrigation policies and provides options for implementing water-price reforms in Gaoyou, China. In Aksu River Basin of Northwest China, the implementation of water-saving measures reduced irrigation quota by up to 48% (Hu et al 2019). Zhou et al (2020aZhou et al ( , 2020b proposed an integrated high-efficient irrigation strategy for water-saving and quality-improving of cash crops.…”

Section: Introductionmentioning

confidence: 99%

Review of agricultural water-saving policies and measures in recent years – a case study of Jiangsu Province, China

Yang

Pu²,

Weng

et al. 2022

Water Supply

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The scarcity of water resources has posed serious threats to the sustainable development of agriculture. China is a largely agricultural country with a large population, and agricultural water consumption accounts for more than 50% of the total water consumption. The application of water-saving irrigation is the main way to realize the sustainable development of water resources and economics. To comprehensively improve crop water-use efficiency and reduce agricultural water consumption, the Chinese central government had promulgated a series of agricultural water-saving policies since 2012, such as strengthening water management, increasing financial investment, and strengthening project construction and operation management and protection. Taking Jiangsu as an example, this paper reviews the main water-saving policies and measures of governments after 2012, as well as the conflicting interest between different water-saving participants. Results showed that, through water-saving policies and measures, its effective utilization coefficient of agricultural irrigation water (EUCAIW) has increased from 0.59 in 2,014 to 0.614 in 2019. By 2019, the area of irrigated arable land controlled by water-saving technologies is 2.848 million hectares, accounting for 67.8% of the irrigated arable land area in Jiangsu. Jiangsu's water-saving policies have been implemented well, and it has guidance on water-saving reform work in other places.

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Degradation of agricultural drainage water quantity and quality due to farmland expansion and water-saving operations in arid basins

Cited by 35 publications

References 32 publications

Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Variabilidade espaço-temporal da qualidade da água em área de agricultura irrigada

Review of agricultural water-saving policies and measures in recent years – a case study of Jiangsu Province, China

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