Agro-Eco-Resource Zonation (AERZ) for sustainable agriculture using GIS and AHP techniques in Indian Punjab

IF 2.8 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Theoretical and Applied Climatology Pub Date : 2024-07-22 DOI:10.1007/s00704-024-05104-4
Prabhjyot-Kaur, Samanpreet Kaur, Abhishek Dhir, Harsimran Kaur, B. B. Vashisht
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Abstract

Punjab state has set the standard for agricultural growth and was the initiator of the Green Revolution in India, producing the highest quality wheat, rice, cotton and other crops. Although Punjab’s land is fertile but over time, but ecological resources, i.e. climate, soil and groundwater have got altered due to which farmers are still unaware to decide which crops are best to grow in their areas for the highest yields and highest financial returns. Agro-Eco-Resource zonation (AERZ) is a classification technique that uses meteorological, soil and groundwater data of the targeted region for proper zonation with higher priority towards crop suitability factors. In the present study 8 major factors (minimum temperature, maximum temperature, rainfall, soil organic carbon, soil texture, soil pH, groundwater depth and groundwater level fluctuations) were used for creating zones for the entire state of Punjab. Analytical Hierarchy Process (AHP) was used for assigning weightage to each factor and the inverse distance weighting interpolation was used to create the thematic maps after importing the data into a Geographic Information Systems (GIS) using attribute tables. These maps were then superimposed so that it would be possible for zoning by using all the stated factors. Based on the degree of suitability for agriculture from highest to lowest, AERZ were identified on a final map. The AERZ map were then validated by comparing them with the rice and wheat productivity data maps. The state was classified into 4 major classes, wherein the zone 4 had conditions most suitable for agricultural crops while zone 1 had constraints and so was least suitable for cultivating crops. The geographical area under zone 3 was nearly 50% followed by zone 4 (30%), zone 2 (19%) and zone 1 (0.7%). The roadmap for sustaining the agricultural productivity in the state revealed that cultivation of resource intensive crops (Rice/ Basmati rice, Wheat, Maize and Vegetables) should be curtailed to only 30% (zone 4) of the total geographical area of the state. Further, the agro-eco-resource constrained ~ 1% (zone 1) of the land area may preferably be kept fallow or used for forest cover. Therefore, AERZ using AHP and GIS techniques is a useful tool for agricultural planning and management. It helps in identifying the best crops that can be grown in a particular region, which in turn helps in improving the efficiency and productivity of agricultural production.

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利用地理信息系统和 AHP 技术在印度旁遮普邦进行农业生态资源区划 (AERZ) 以促进可持续农业发展
旁遮普邦制定了农业发展的标准,是印度绿色革命的发起者,生产出最优质的小麦、大米、棉花和其他作物。虽然旁遮普的土地肥沃,但随着时间的推移,气候、土壤和地下水等生态资源已经发生了变化,因此,农民仍然不知道在自己的地区种植哪种作物最能获得最高产量和最高经济回报。农业生态资源分区(AERZ)是一种分类技术,利用目标区域的气象、土壤和地下水数据进行适当分区,优先考虑作物适宜性因素。在本研究中,使用了 8 个主要因素(最低气温、最高气温、降雨量、土壤有机碳、土壤质地、土壤 pH 值、地下水深度和地下水位波动)为整个旁遮普邦创建分区。在使用属性表将数据导入地理信息系统(GIS)后,使用层次分析法(AHP)为每个因子分配权重,并使用反距离加权插值法绘制专题地图。然后将这些地图叠加在一起,这样就可以利用所有所述因素进行分区。根据从高到低的农业适宜程度,在最终地图上确定了农业经济区。然后,通过与水稻和小麦生产力数据地图进行比较,对农业经济区地图进行验证。该州被划分为 4 个主要等级,其中第 4 区的条件最适合种植农作物,而第 1 区则受到各种限制,因此最不适合种植农作物。第 3 区的面积占近 50%,其次是第 4 区(30%)、第 2 区(19%)和第 1 区(0.7%)。维持该州农业生产力的路线图显示,资源密集型作物(水稻/巴斯马蒂水稻、小麦、玉米和蔬菜)的种植面积应减少到该州总面积的 30%(第 4 区)。此外,农业生态资源有限的 ~ 1%(第 1 区)土地最好保持休耕或用于森林覆盖。因此,使用 AHP 和 GIS 技术的 AERZ 是农业规划和管理的有用工具。它有助于确定特定区域可种植的最佳作物,进而有助于提高农业生产的效率和生产力。
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来源期刊
Theoretical and Applied Climatology
Theoretical and Applied Climatology 地学-气象与大气科学
CiteScore
6.00
自引率
11.80%
发文量
376
审稿时长
4.3 months
期刊介绍: Theoretical and Applied Climatology covers the following topics: - climate modeling, climatic changes and climate forecasting, micro- to mesoclimate, applied meteorology as in agro- and forestmeteorology, biometeorology, building meteorology and atmospheric radiation problems as they relate to the biosphere - effects of anthropogenic and natural aerosols or gaseous trace constituents - hardware and software elements of meteorological measurements, including techniques of remote sensing
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