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This paper analyzes the effects of climatic variables on food crop yields at the prefectural level from 1996 to 2016 in Togo. Using the panel‐corrected standard errors method and panel data from departments in charge of agricultural statistics and meteorology, the results show that meteorological variables have various effects on food crops yields, but the negative effects are dominant. In addition, adaptation strategies through agricultural land reallocation have not enabled farmers to improve food crops yields. There is an urgent need for public authorities to implement actions to strengthen farmers' resilience through practice adoption and cultural innovations for adaptation to climate change. [ABSTRACT FROM AUTHOR]

This paper examines the impact of climate change on the decision of farmers to engage or not to engage in livestock activities and also on the choice of different livestock species in Kenya. To this end, cross-sectional household level data supplemented by long-term averages of climate data are used. The probit model is employed to derive the response of the probability of engaging in livestock activities to climate change. Probit and multivariate probit methods are employed to model the choice of different livestock species. Atmosphere–ocean global circulation models are used to project the impact of different climate scenarios on the probability of engaging in livestock activities and also of adopting different livestock species according to variations in climate. The results suggest that farmers adapt livestock management decisions to climate change. At low levels of temperature increase, the probability of engaging in livestock activities falls, but at higher levels of climate change, the probability rises. The results further show that as it gets hotter, farmers change their livestock choices from dairy cattle and sheep to beef cattle and goats. [ABSTRACT FROM AUTHOR]

All over the world, farming communities need to adapt to a changing climate. At the same time, they are confronted by the necessity to increase food availability for a growing population, but also to ensure a sustainable use of natural resources. In this process, agriculture is not just the fiend responsible for an increase in greenhouse gas emissions, nor the victim of rising temperatures and extreme weather events. Agriculture is also an ally in climate change mitigation and adaptation because the selection of ad hoc crop varieties and livestock can lower the environmental impact of farming and the implementation of better management practices can promote soil conservation. Whether agriculture will have a positive or a negative impact on climate change adaptation and mitigation will depend on the rural policies implemented, but it will also be contingent on an adequate understanding of the interconnections existing between weather, climate, and farming. This review explores such interconnections by focusing on the history of agricultural meteorology, which is the research field that studies the impact of weather and climate on crops, livestock, farming operations, and plant and animal pests and diseases. The article discusses stakeholders, institutions, and main developments in agricultural meteorology, and describes how the agenda of agricultural meteorology has shifted over time. At the beginning of the 20th century, research in agricultural meteorology focused on increasing the profitability of farming. In the 21st century, instead, the main goal of agricultural meteorology is to ensure food security and guarantee sustainability. This article is categorized under: Climate, History, Society, Culture > Disciplinary Perspectives [ABSTRACT FROM AUTHOR]

Water resources management and agriculture planning models require a statistical synthesis of some rainfall features, in particular those representing dry atmospheric conditions. The bigger the basin, the more important these features become, as is the case of the La Plata Basin LPB.This paper focuses on the precipitation variability in the large LPB in South America, analysing the number of months per year with low rainfall and the sequences of months with low rainfall, their theoretical distributions and stability, which are needed as input for the models mentioned above.Long time series are used to analyse the lowfrequency variability and the relative importance of decadal variability. Changes are evident in the number of months per year with low rainfall, with a decrease of about 20 in the period after 1970.Theoretical distribution models binomial and geometric are fitted to these empirical distributions, and the regional variability of the fitting parameters is shown. In practically the entire region, the goodnessoffit of the two theoretical models considered is statistically satisfactory.The temporal variability of the parameters of the theoretical binomial p and geometric α distributions is analysed, in excluding subperiods of 10 and 5 years, respectively. The results show lowfrequency variability overlapped on a decadal variability, with low homogeneous regional behaviour.The distribution models have proven to be efficient for frequency adjustments of the rainfall properties studied. These results are an acceptable and necessary input to decision models in LPB. They also make it possible to infer effects of climate change. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

We study potential impacts of future climate change on U.S. agricultural productivity using county‐level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO‐OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950–89. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%. [ABSTRACT FROM AUTHOR]

Climate change has a significant impact on agrometeorological conditions. We analysed the winter wheat overwintering indices for the northern winter wheat production areas of China (NWPA), which include the percentage of extreme low‐temperature (daily minimum temperature ≤−22 °C) years, the first day of the overwintering period (FDO), sowing date (SD) and precipitation before winter (PBW). Then, we compared the differences of the four indices between the forecast period 2021–2050 and the baseline period 1961–1990 under the RCP4.5 scenario, which were based on the 0.5° × 0.5° grid daily meteorological data output provided by the Inter‐Sectoral Impact Model Intercomparison Project. The results showed that the occurrence of extreme cold winters decreases the NWPA, which will result in the northern boundary of winter wheat moving north by 1–2 latitudes and in an increase in the potentially safe overwintering areas by 8.5%. The FDO will be delayed in 2021–2050 by an average of 7.7 days. Climate warming will change the meteorological conditions for winter wheat overwintering in the Huang‐Huai winter wheat area, especially near the southern boundary of the winter wheat planting area, and the areas and years with no obvious overwintering periods will experience an increase by 50% and 56%, respectively. The SD will be postponed by 8.6–9.0 days. The PBW will decrease as the accumulated temperature increases, especially in the Huang‐Huai winter wheat area. In the future, the possible northern boundary for winter wheat planting may move northward, and the northernmost tip may reach 46°N. South of the Huang‐Huai region, winter wheat planting may change from a semi‐winter to a spring variety. These findings will provide a reference for generating wheat planting and management strategies in China. Climate change has significantly influenced the winter wheat planting in northern China. In 2021–2050, climate warming would decrease the occurrence of extreme cold winters in the regions near northern planting boundary, which would result in the possible northern boundary (red and green lines) moving northward, and the northernmost tip may reach 46°N. However, south of the Huang‐Huai region (black oval areas), wheat planting may change from a semi‐winter to a spring variety due to temperature increase and precipitation reduction. [ABSTRACT FROM AUTHOR]

In third world countries like Indonesia, higher education is expected to contribute to the development of applied sciences that tackle pressing problems. Adaptation to and mitigation of climate change are such fields. Applied sciences work better in problem solving when approached in an interdisciplinary way. Winarto and Stigter have decades of experience in applied agricultural sciences but from different main fields: anthropology and physics/(micro)meteorology/(micro)climatology. They provide a welcome complementarity in their dialogue-based interactions with farmers on how climate change has necessitated livelihood/resilience changes for them. They developed and established climatological services for farmers, consisting of seven initial climate services for agriculture, bringing farmers closer to their ecosystems that are currently threatened by climate change. They established what they call 'agrometeorological learning,' which is about changes of beliefs, attitudes, behaviors, and goals of farmers due to new meteorological and climatologic knowledge they acquire, which considerably help increase farmers resiliencies. [ABSTRACT FROM AUTHOR]

It is well established that crop production is inherently vulnerable to variations in the weather and climate. More recently the influence of vegetation on the state of the atmosphere has been recognized. The seasonal growth of crops can influence the atmosphere and have local impacts on the weather, which in turn affects the rate of seasonal crop growth and development. Considering the coupled nature of the crop–climate system, and the fact that a significant proportion of land is devoted to the cultivation of crops, important interactions may be missed when studying crops and the climate system in isolation, particularly in the context of land use and climate change. To represent the two-way interactions between seasonal crop growth and atmospheric variability, we integrate a crop model developed specifically to operate at large spatial scales (General Large Area Model for annual crops) into the land surface component of a global climate model (GCM; HadAM3). In the new coupled crop–climate model, the simulated environment (atmosphere and soil states) influences growth and development of the crop, while simultaneously the temporal variations in crop leaf area and height across its growing season alter the characteristics of the land surface that are important determinants of surface fluxes of heat and moisture, as well as other aspects of the land-surface hydrological cycle. The coupled model realistically simulates the seasonal growth of a summer annual crop in response to the GCM's simulated weather and climate. The model also reproduces the observed relationship between seasonal rainfall and crop yield. The integration of a large-scale single crop model into a GCM, as described here, represents a first step towards the development of fully coupled crop and climate models. Future development priorities and challenges related to coupling crop and climate models are discussed. [ABSTRACT FROM AUTHOR]