Study on Modeling and Evaluating Alfalfa Yield and Optimal Water Use Efficiency in the Agro-Pastoral Ecotone of Northern China

Author:

Miao Xiangyang12,Wang Guoshuai23,Li Ruiping1,Xu Bing23,Zheng Hexiang23,Tian Delong23,Wang Jun23,Ren Jie23,Li Zekun23,Zhou Jie12

Affiliation:

1. College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

2. Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

3. Institute of Water Resources for Pastoral Area Ministry of Water Resources, Hohhot 010020, China

Abstract

The agro-pastoral ecotone in northern China is the main production area of agriculture and animal husbandry, in which agricultural development relies entirely on groundwater. Due to the increasing water consumption of groundwater year by year, groundwater resources are becoming increasingly scarce. The substantial water demand and low germination rate in the first year are the main characteristics of alfalfa (Medicago sativa L.) yield in the agro-pastoral ecotone in northern China. Due to unscientific irrigation, water resources are seriously wasted, which restricts the development of local agriculture and animal husbandry. The study constructed the Dssat-Forages-Alfalfa model and used soil water content, leaf area index, and yield data collected with in situ observation experiments in 2022 and 2023 to calibrate and validate the parameters. The study found ARE < 10%, ENRMS < 15%, and R2 ≥ 0.85. The model simulation accuracy was acceptable. The study revealed that the water consumption at the surface soil layer (0–20 cm) was more than 6~12% and 13~31% than that at the 20–40 cm and 40–60 cm soil layers, respectively. The study showed when the irrigation quota was 30 mm, the annual yield of alfalfa (Medicago sativa L.) (7435 kg/ha) was consistent with that of the irrigation quota of 33 mm, and increased by 3.99% to 5.34% and 6.86% to 10.67% compared with that of irrigation quotas of 27 mm and 24 mm, respectively. To ensure the germination rate of alfalfa (Medicago sativa L.), it is recommended to control the initial soil water content at 0.8 θfc~1.0 θfc, with an irrigation quota of 30 mm, which was the best scheme for water-use efficiency and economic yield. The study aimed to provide technological support for the rational utilization of groundwater and the scientific improvement of alfalfa yield in the agro-pastoral ecotone in northern China.

Funder

Study on the Water Cycle Mechanism of Typical Irrigation Areas in the Agro-pastoral Ecotone of Yinshanbeilu Mountains in Inner Mongolia

Technical Demonstration of Collaborative Utilization of Water–Soil–Plant Resources System in Ecological Agriculture and Animal Husbandry Area

Study and Demonstration of the Key Technologies for Efficient Water-saving in the Forage Area of the Yellow River Basin in Inner Mongolia

Key Projects of DaMaoQi Irrigation Experimental Station

The Project of Monitoring and experimenting study of Yinshanbeilu Grassland Eco-hydrology National Observation and Research Station Field testing station

Inner Mongolia National Natural Science Foundation

Evolutionary characteristics of groundwater resources in Western Ordos and integrated demonstration of agricultural deep water-saving and efficient utilization technology

Research and integrated demonstration of key technologies for seedling preservation, rain storage, supplementary irrigation and steam reduction in dryland agriculture in eastern Mongolia

Publisher

MDPI AG

Reference41 articles.

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3. Adaptability of 43 Medicago sativa varieties in the irrigated agricultural area of Zhangye, Gansu Province;Quan;Pratac. Sci.,2023

4. Crops caloric value and adjustment of planting structure in rainfed farmland of north Yinshan Mountian area, Inner Mongolia;Duan;J. Arid Land Resour. Environ.,2013

5. Analysis of Water Requirement and Water Requirement Law of Main Crops in Dryland Area at The Northern Foot of Yinshan Mountain;Kang;J. Tianjin Agric. Sci.,2023

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