Adaptive policy analysis of sustainable and productive agriculture and its context

According to the World Bank, agriculture can help raise incomes, reduce poverty, and ensure better food security for 80% of the world’s poor, who mostly work in farming and live in rural areas. This study sought to show that rational sustainable and productive agriculture (SPA), and a favorable macro environment can minimize agriculture trade-offs and enhance synergies. This analysis aims to clarify the links between country agriculture and its context, leading to evidence-based recommendations for efficiency and sustainability designed to promote the rational development of SPA. The Performance Model of Sustainable Agriculture of the World (PEMSA model), sub-models, and a map developed in this study revealed that improvements in countries’ political and social indicators lead to improvements in their SPA. Our evidence supports the idea that environmental, political, legal, planning, social, and economic sustainability metrics usually behave in a similar fashion and follow the same general course in their movement. The performance model developed in this study to analyze links between SPA and its macro environment provides evidence-based digital recommendations and strategies that will help to improve SPA; the model explains 99.4% of SPA dispersions. The findings reveal a synergistic effect for SPA (67.7%) through improving a country’s macro environment. Integrated multiple-criteria and statistical analysis was used to simulate global SPA policy by analyzing 1925 scenarios. It can also help to define the best means toward progress sustainable and productive agriculture. The world is full of data, and the amounts are growing; yet, we are still surprisingly in a data shortage for some agricultural actions and processes. Data are inadequate or arrive too late in too many countries, and fail to cover some issues in sufficient detail when available. Decision-makers that face data shortage and completeness problems when working with traditional sources of agriculture data can use Google data to overcome these issues. The developed PEMSAGoogle models have shown sufficient average reliability (R2aver=0.933–0.956). Cost-effective and real-time data on stakeholders' opinions from Google can help decision-makers overcome the data shortage and completeness problems they face when working with traditional sources of agricultural management data.