数字农业和灌溉潜力的多元评估：在印度的应用

IF 6.1 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Agricultural Systems Pub Date : 2025-04-10 DOI:10.1016/j.agsy.2025.104328

Satyajit Dwivedi, Mazhuvanchery Avarachen Sherly

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引用次数: 0

摘要

根据Grand View的一份研究报告，到2022年，全球精准农业市场的价值将达到69.6亿美元，预计到2030年将以12.8%的年增长率增长。数字农业和灌溉技术通过优化用水，为提高农业生产力和加强粮食安全提供了巨大潜力，每年可节约高达3000亿立方米的水，产生约11万亿美元的经济效益。然而，缺乏统一的国家或区域战略来突出具体的区域潜力和优先考虑投资，可能导致资源配置效率低下和回报降低。本研究引入了两个复合指标，即数字农业潜力指数（DAPI）和数字灌溉潜力指数（DIPI），旨在评估采用数字农业和灌溉技术的区域准备程度和潜力。目标是支持在农业领域有效部署数字解决方案的国家战略。方法ddapi评估数字化准备程度和农业强度，而DIPI纳入了农业用水压力的额外测量。这些指标采用主成分分析（PCA）和数据包络分析（DEA）构建，以实现对区域能力的结构化和客观评价。结果和结论对印度各邦的比较分析揭示了显著的地区差异。古吉拉特邦、马哈拉施特拉邦、泰米尔纳德邦、卡纳塔克邦和喀拉拉邦等拥有先进信息技术政策的邦显示出高度的数字化准备程度和采用数字农业解决方案的巨大潜力。相比之下，恒河平原和炎热半干旱地区的邦，如北方邦、比哈尔邦、贾坎德邦和恰蒂斯加尔邦，由于农业强度高，但数字化准备程度低，面临着挑战。西部和北部地区也面临着严重的水资源压力。这些发现强调了有针对性地改进IT政策和投资以弥合数字采用差距的必要性。发展DAPI和DIPI作为国家战略的一部分，可以指导政策制定者优先投资数字农业和灌溉。通过解决准备程度方面的差距，这些指标可以促进优化水资源管理，提高农业生产力，并为整个印度的可持续发展做出贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Multivariate assessment of digital agriculture and irrigation potential: Application to India

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Multivariate assessment of digital agriculture and irrigation potential: Application to India

CONTEXT

According to a Grand View research report, the global precision farming market, valued at USD 6.96 billion in 2022, is projected to grow at an annual growth rate of 12.8 % through 2030. Digital agriculture and irrigation technologies offer substantial potential to increase agricultural productivity and enhance food security by optimizing water use, potentially saving up to 300 billion m³ of water annually and generating economic benefits of approximately USD 11 trillion. However, the absence of cohesive national or regional strategies to highlight specific regional potentials and prioritize investments risks inefficient resource allocation and reduced returns.

OBJECTIVE

This study introduces two composite indicators, the Digital Agriculture Potential Index (DAPI) and the Digital Irrigation Potential Index (DIPI), designed to assess regional readiness and potential for adopting digital agriculture and irrigation technologies. The objective is to support national strategies for the effective deployment of digital solutions in agriculture.

METHOD

DAPI assesses digital readiness and agricultural intensity, while DIPI incorporates an additional measure of agricultural water stress. These indices were constructed using Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA) to enable structured and objective evaluations of regional capacities.

RESULTS AND CONCLUSIONS

A comparative analysis across Indian states revealed significant regional variations. States with advanced IT policies, such as Gujarat, Maharashtra, Tamil Nadu, Karnataka, and Kerala, demonstrated high digital readiness and strong potential to adopt digital agriculture solutions. In contrast, states in the Gangetic plains and hot semi-arid regions, such as Uttar Pradesh, Bihar, Jharkhand, and Chhattisgarh, face challenges due to high agricultural intensity but low digital readiness. Western and northern regions also contend with substantial water stress. These findings underscore the need for targeted improvements in IT policies and investments to bridge digital adoption gaps.

SIGNIFICANCE

The development of DAPI and DIPI as part of a cohesive national strategy can guide policymakers in prioritizing investments in digital agriculture and irrigation. By addressing gaps in readiness, these indicators can facilitate optimized water management, bolster agricultural productivity, and contribute to sustainable development throughout India.

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来源期刊

Agricultural Systems 农林科学-农业综合

CiteScore

13.30

自引率

7.60%

发文量

174

审稿时长

30 days

期刊介绍： Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments. The scope includes the development and application of systems analysis methodologies in the following areas: Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making; The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment; Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems; Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.