Climate Change Impacts and Strategies for Mitigation and Adaptation in Agriculture

Agriculture is highly vulnerable to climate change-induced shifts in means, variability and extremes [...]

In the second paper, Zhao et al. [3] estimate the impact of climate change on crop yield focusing on spring maize in Jilin Province, China, which is the Chinese "golden corn belt". This study uses daily weather data from 50 meteorological stations and annual statistics on county level maize yield and planted area. The strength of this paper is the use of high-stability of meteorological yield to improve statistically significant climate indicators. Moreover, the paper divides the crop yield into two types: short-term meteorological yield fluctuation and crop longer term management-induced crop yield. They find maize is beginning to relocate with late-maturing varieties moving north and east, while the areas exhibiting higher yields have moved eastward. Such a northward shift was also observed for US maize [10]. Moreover, they indicate that the eastern region has dependable sunshine in September, while the western region is strongly influenced by the number of sunshine hours. Additionally, they find climatic suitability is mostly affected by extremes (i.e., drought and chilling), especially in the eastern and western regions. They recommend that future research incorporates extremes in suitability assessments.
In the third piece, Bagagnan et al. [4] use farm-level data in the Central River Region of the Gambia to examine farmer perceptions of climate variability and the use of adaptation measures. They indicate that the region rainfall is irregular and unpredictable. This paper contains several interesting aspects. The first aspect is the study location, which is considered to be the "food basket" of the Gambia. The second is the focus on how farmers perceive their exposure to climatic change and possible losses. The third aspect is the investigation of farmers' preferences regarding the cost and effectiveness of adaptation measures. Lastly, the study combines data collected from farm households through transect walks and focus group discussions. Several important findings arise, for example, a majority of their sampled farmers perceive an increase in heat and extremes and that they are vulnerable to drought, as also found in India, Burkina Faso, and Myanmar [11][12][13]. Next, while the use of chemical fertilizers is the most expensive adaptative measure, a majority of the sampled farmers believe that it is the most effective measure. They also find smallholder farmers are more likely to apply chemical fertilizers than are large-scale ones. Finally, they find that farmers only apply chemical fertilizers when they expect favorable weather conditions and that crop rotation is the most implemented adaptation measure.
In the fourth paper, Kapilakanchana and McCarl [5] consider the role of agricultural technological progress and the use of bioenergy as a mitigation option in the US. The paper examines scenarios that simultaneously vary agricultural technological progress, the US renewable fuel standard (RFS), and agricultural demand growth as they influence crop, livestock and bioenergy markets; land use allocation; and greenhouse gas (GHG) emissions. They employ econometric models with several functional forms to investigate the role of technological progress on the major crop yields. Then, nine main scenarios are simulated using a dynamic simulation model. Several interesting findings arise. For example, the article reveals that technological progress negatively influences land use for biofuel and pasture, while it positively affects land use for crop production. Additionally, they find the prices of most crops will increase over time (except for soybean), whereas the broiler price is projected to increase after 2020. Moreover, technological progress of main field crops does not statistically determine their prices. In addition, increasing technological progress tends to reduce overall GHG emissions. This finding is supported by Khanna et al. [14], who found that the bioenergy from corn has a lower carbon intensity than gasoline and technological improvements have made bioenergy from corn increasingly competitive. Finally, they indicate that technological improvement is a key factor in meeting growing global demand for food and energy and reducing emissions. Their study provides a key takeaway to policy makers that technological improvement is a greenhouse gas mitigation approach.
The last two articles estimate the benefit of publicly provided climate and weather information. Specifically, Rhodes and McCarl [6] estimate the value of ocean-related decadal climate variability (ODCV) information in the US, going beyond previous studies that mostly evaluated the value of forecasts for El Niño Southern Oscillation (ENSO). An interesting feature of this paper is the combination of the econometric panel data yield models with a stochastic agricultural sector nonlinear optimization model. The paper firstly econometrically estimates crop yield impacts and, in turn, the value of releasing ODCV information on crop yields with eight joint ODCV phase combinations. Then, the authors plugged in the changes in crop yields into a US-wide agricultural sector model. The methods used in this study can easily be adjusted to handle different indices and extended to evaluate the economic benefits of other systematic weather influencing phenomena. The study shows USD 86 million annual welfare gains when using conditional forecasts and welfare gains of USD 1.1 billion annually under perfect forecasts. Therefore, they indicate that public spending on ODCV information is likely merited.
Lastly, Lin et al. [7] estimate the economic value of meteorological information services for Taiwanese farmers from a study over 400 registered farmers in 20 municipalities. They use a contingent valuation method. Their study provides new contributions to the literature in using nonmarket valuation mechanisms to elicit information on the economic value of the Taiwanese meteorological information service. This article also shows how to construct a calibration model that reduces starting point bias for censored data. The study reveals that agricultural producers' willingness to pay (WTP) is determined by weather forecast accuracy, farm size, and first bid price. Moreover, they find that annual WTP ranges from USD 56.06 to USD 90.92 and, in turn, that the annual economic value of meteorological information services for agricultural producers in Taiwan is between USD 28.06 million and USD 45.51 million.
Collectively, this group of articles updates the reader on the impacts of climate change on crop yields, possible mitigation and adaptation options for several regions, the use of modern techniques, models and unique data. We thank the authors for their excellent contributions and hope that this Special Issue stimulates ideas, collaboration, and a means to move us forward toward a more resilient agriculture that can better cope with a changing climate.