Agricultural adaptation to climatic variation

Smit, Barry; McNabb, David E.; Smithers, John

doi:10.1007/bf00140511

Cited by 181 publications

(112 citation statements)

References 26 publications

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“…Although agricultural productivity varies as a function of land use and management issues (Rounsevell et al, 1999), forecasting future agricultural yields requires explicit attention to climatic variability (Smit et al, 1996) and climatic conditions on spatial scales much smaller than those offered by the most highly resolved general circulation models (GCMs). Extended periods of high air temperature with below normal precipitation can cause moisture stress to agricultural crops, such as corn and soybeans, and negatively impact yields (Carlson, 1990).…”

Section: Introductionmentioning

confidence: 99%

Downscaling temperature and precipitation: a comparison of regression‐based methods and artificial neural networks

Schoof

Pryor

2001

Intl Journal of Climatology

238

148

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A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are indices of the synoptic scale circulation derived from rotated principal components analysis (PCA) and cluster analysis of variables extracted from an 18-year record from seven rawinsonde stations in the Midwest region of the United States. PCA yielded seven significant components for the growing season and five significant components for the non-growing season. These PCs explained 86% and 83% of the original rawinsonde data for the growing and non-growing seasons, respectively. Cluster analysis of the PC scores using the average linkage method resulted in eight growing season synoptic types and twelve non-growing synoptic types. The downscaling of temperature and precipitation is conducted using PC scores and cluster frequencies in regression models and artificial neural networks (ANNs).Regression models and ANNs yielded similar results, but the data for each regression model violated at least one of the assumptions of regression analysis. As expected, the accuracy of the downscaling models for temperature was superior to that for precipitation. The accuracy of all temperature models was improved by adding an autoregressive term, which also changed the relative importance of the dominant anomaly patterns as manifest in the PC scores. Application of the transfer functions to model daily maximum and minimum temperature data from an independent time series resulted in correlation coefficients of 0.34 -0.89. In accord with previous studies, the precipitation models exhibited lesser predictive capabilities. The correlation coefficient for predicted versus observed daily precipitation totals was less than 0.5 for both seasons, while that for monthly total precipitation was below 0.65. The downscaling techniques are discussed in terms of model performance, comparison of techniques and possible model improvements.

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Section: Introductionmentioning

confidence: 99%

Downscaling temperature and precipitation: a comparison of regression‐based methods and artificial neural networks

Schoof

Pryor

2001

Intl Journal of Climatology

238

148

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“…However, because governments have responded to past droughts with disaster relief packages, there may be little incentive for farmers to take defensive action themselves. This insulation against the effects of an anticipated extreme event could promote what Smit et al 44 describe as maladaptation to climate change. Such a situation actually took place in summer 1988, with an El Niño-induced drought requiring federal support payments (including crop insurance) in excess of $1.3 billion (Canadian), an amount further supplemented by provincial support programs.…”

Section: Moving Beyond Assumptions About Adaptabilitymentioning

confidence: 99%

“…In one study, an empirical analysis of 120 southwestern Ontario farmers, 44 it was found that although the majority of respondents reported being affected by variable climatic conditions over a 6-year study period, only one in five made any conscious effort to adjust their operations in light of climatic events. Similar findings are emerging from an ongoing study of agricultural adaptation to climate on a sample of farms in southern Alberta.…”

Section: Moving Beyond Assumptions About Adaptabilitymentioning

confidence: 99%

Climate Change and the Adaptability of Agriculture: A Review

Johnston

Chiotti

2000

Journal of the Air & Waste Management Association

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The assessment of climate change impacts on agriculture has emerged as a recognizable field of research over the past 15 years or so. In a relatively short period, this area of work has undergone a number of important conceptual and methodological developments. Among many questions that have been debated are the adaptability of agriculture to climate change and the importance of land management adjustments in reducing the adverse effects of climate change. In turn, this latter focus has spawned a discussion regarding the nature of adaptation and the ability of agriculture to respond to sudden and rapid climatic changes.

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“…Small holder farmers have in many years been depended on favourable rainfall patterns for a good yield in Zimbabwe. Farming communities do not only respond to rainfall quantity, but also to variations in time, as a result, the relatively small changes in rainfall frequency may have strong effects on communities [1][2][3]. The farmers mostly practise rain-fed crop farming with a handful irrigating as this situation is driven by lack of resources and funds to have the irrigation infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Seasons Analysis as a Guiding Tool to Small Holder Farmers in The Face of Climate Change in Midlands in Zimbabwe

Fm¹,

J²

2017

J Earth Sci Clim Change

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A rigorous analysis of trends in rainfall season of an area over a lengthy period of time can be effectively used as a tool to guide small holder farmers in their activities. Climate change adaptation is important to ensure sustainability and survival in rainfed crop farming. In this study two districts (Chirumhanzu and Zvishavane) in the Midlands Province of Zimbabwe were selected and rainfall data for the areas was provided by Zimbabwe Meteorological Services Department. Rainfall characteristics that include onset times, cessation times, length of seasons, total amount of rainfall, number of rainy days, dry spell and risk of water logging were analysed to provide farmers with an indicative picture of possibilities in their respective areas in a bid to reduce risks of crop failure due to uncertainties brought about by climate change and variability. Time series analysis method together with risk analysis were the statistical methods employed to understand better the risk and measures that can be taken by farmers. For Chirumhanzu it was concluded there is a 41% risk of replanting if farmers plant with the first rains received. However, over the period this risk seems to be decreasing. Farmers are encouraged to stagger the planting to minimise risk. Risk analysis showed that for Zvishavane the mean number of rainy days for Zvishavane is 34 with a possibility of 45 days in good seasons and 23 days in poor seasons The tool guides meant for farmers require proper packaging and systematic channels to promote buy-in by the end users of the information.

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Agricultural adaptation to climatic variation

Cited by 181 publications

References 26 publications

Downscaling temperature and precipitation: a comparison of regression‐based methods and artificial neural networks

Downscaling temperature and precipitation: a comparison of regression‐based methods and artificial neural networks

Climate Change and the Adaptability of Agriculture: A Review

Rainfall Seasons Analysis as a Guiding Tool to Small Holder Farmers in The Face of Climate Change in Midlands in Zimbabwe

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