Economic Modeling of Climate-Smart Agriculture in Iran

Abstract

Global warming is the immediate consequence of increased greenhouse gasses emission. Agriculture is a significant source in terms of greenhouse gasses emission and on the other hand, the main sector in terms of producing food. As global food demand grows, the share of agriculture in the total greenhouse gasses emission will rise too. Therefore, agriculture needs to cut the greenhouse gasses emission. A response to the two important issues today, i.e. achieving food security and reducing greenhouse gasses emission is climate-smart agriculture. According to the Paris Agreement, an international effort to reduce greenhouse gasses emission, Iran has to decrease 12 percent of its greenhouse gasses emission by 2050, which all sectors have to contribute. Since the pathway to define strategies, is to explore the challenges; in this study, a seemingly unrelated regression technique has been used to model the climate-smart agriculture in Iran. Three main sub-sectors of agriculture; i.e. crops, livestock, and aquatics production, have been considered in the model to find the role of them in delivering food security and emitting greenhouse gasses. The findings show livestock and aquaculture sectors have had a positive significant impact in achieving food security. On the other hand, these sectors have had a positive significant effect on the emitting greenhouse gasses. Cropping system was not found to have a significant role in achieving food security and emitting greenhouse gasses in Iran although the expected signs (+) has been confirmed by the model. New research to explore appropriate technical and behavioral innovations needs to do on the specific-product-sector to be climate-friendly and sustainable. On the consumers hand, an encouragement to a more healthy diet with more vegetable, where is possible, also can reduce emissions. Finally, the key message from the assessments is the future legislative outlines for mitigation, adaptation and resource management as well as consumer behavior for how agriculture can deal with climate change.

A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

“…Previous systematic reviews explored the role of CSA [ 91 , 93 , 94 , 95 ]. Related research discusses technological adoption [ 96 ], farmers’ adaptation to resilient agriculture [ 97 ], innovative economic models [ 98 ], emerging ways to increase the resilience of smallholder farmers [ 99 ], and the impact of CSA on food security [ 100 ], among other issues associated with resilient agriculture.…”

New Evaluation of Small Farms: Implication for an Analysis of Food Security

“…Small farms need appropriate international collaboration and the sharing of experiences in different regions of the world. Since Innovations and interventions [48] can have different consequences, improving food security needs an adequate (technical, social and economic) innovation [49][50][51] in farms.…”

Agriculture 4.0 and Smart Sensors. The Scientific Evolution of Digital Agriculture: Challenges and Opportunities

“…This high-density motor theme is related to sustainable agriculture, which shows its strong relationship with climate-related themes (e.g., 'CLIMATE-CHANGE', 'CLIMATE-CHANGE-ADAPTATION'), environmental preservation (e.g., 'AGROFORESTRY', 'CONSERVATION-AGRICULTURE'), organizations (e.g., 'SMALLHOLDER FARMERS') and discussions related to agricultural resilience, food security and the main challenges for organizations, especially for smallholders. Related research discusses technological adoption [87], farmers' adaptation to resilient agriculture [88], economic models [89], ways to increase the resilience of smallholder farmers [90], and the impact of CSA on food security [91], among other issues associated with resilient agriculture.…”

“…Yield prediction is historically used in agriculture for farmers to plan inputs, labor, profitability and other issues related to agricultural productivity. The cluster 'YIELD-PREDICTION' (Figure 4 (i)) in DA represents the evolution of predictive analysis on crop yields, which in the past was carried out by the knowledge of farmers, and is now carried out with emerging technologies [89]. In this way, preharvest crop yield prediction helps farmers to make better and faster decisions related to planting, fertilization, irrigation, pruning and harvesting plantations [131], also contributes to the entire supply and consumption chain, and allows the projection of future scenarios for food production [132].…”