Climate change induced alterations from historical patterns of precipitation, temperature, and atmospheric gases as well as increases in the frequency of extreme events is leading to alterations in global cereal production and its spatial distribution. Using a US agricultural sector model, we examine effects and acreage adaptation with an emphasis on wheat and the Pacific Northwest region. Use of a national sector model allows for analysis at the national as well as regional level. Generally, under climate change we find that the incidence of wheat production shifts northward in the Southern Great Plains, westward in Northern Great Plains and eastward in Oregon and Washington, all of which are moves to cooler conditions. Total wheat acreage in the Pacific Northwest is expected to decline from 6 million acres under no climate change to 5.4-5.7 million acres over the study period. Additionally, we consider impacts on price, production, and consumer, producer, and foreign welfare finding losses to consumer welfare and gains to producer welfare with overall losses in surplus. Recommendations are made for future research and alternative ways that adaptation strategies can be integrated into models to predict long-term impacts.
Agriculture is quite sensitive to climate change and to date it has been impacted in many ways. In turn, adaptation to lessen the impacts has attracted increasing attention. Here we discuss private and public roles in adaptation, as well as procedures for the evaluation of adaptation projects. Additionally, we discuss adaptation realities and limits that constrain the practical ability of adaptation actions to cope with climate effects.
Many Afghanistan households face food insecurity (FI), and this threatens sustainable development. Policymakers and international donors are trying to alleviate FI using food aid, development assistance, and outreach. This study identified household characteristics that discriminate between food-insecure and food-secure households, facilitating accurate assistance targeting in Afghanistan. We used machine-learning classification models (classification decision tree and random forest model) and applied to a household survey. This was done using equal priors and 1.5:1 misclassification penalties. The resulting model is able to correctly identify 80% of food-insecure households. Characteristics in six major categories are found important. Unsurprisingly traditional key variables, such as (1) income and expenditure items, (2) household size, (3) farm-related measures; (4) access to particular resources, and (5) short term shocks are important determinants of food security level. We also found the relevance of long-term household characteristics, such as dwelling wall composition, which are not generally addressed in the existing literature. We argue that these are reflective of accumulated household wealth and this supports the idea that some factors determining food security are persistent. We also found that commonly used demographic variables were not important.
Climate change undeniably impacts agriculture and natural resources, enterprises and markets. For informed decision making, there is a need for information on climate change adaptation possibilities and mitigation alternatives. Mathematical programming has been used to address the economic aspects of such questions and allows analysis as climate change moves the environment into previously unobserved conditions. It allows us to model spatial and dynamic features of the issue and analyze heretofore unobserved adaptation and mitigation possibilities. This review provides an overview of and references for modeling techniques, conceptual issues, and major assumptions involved with using mathematical programming as a climate change economic analyzing engine, along with a brief comparison with other methods. We also review a number of studies applying mathematical programming to examine climate change impacts, adaptation, and mitigation issues in the agricultural and natural resources arena. Finally, we present a very brief discussion on research needs. Expected final online publication date for the Annual Review of Resource Economics, Volume 15 is October 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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