| Records |
Author  |
Sharif, B.; Olesen, J.E. |
| Title |
Probabilistic assessment of agroclimatic effects on winter rapeseed yield in Denmark |
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Conference Article |
| Year |
2014 |
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CropM |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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MA @ admin @ |
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2828 |
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Sharif, B.; Olesen, J.E.; Schelde, K. |
| Title |
Statistical learning approach for modelling the effects of climate change on oilseed rape yield |
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2014 |
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Statistical learning is a fairly new term referring to a set of supervised and unsupervised modelling and prediction techniques. It is based on traditional statistics but has been highly enhanced inspired by developments in machine learning and data mining. The main focus of statistical learning is to estimate the functions that quantify relations between several parameters and observed responses. These functions are further used for prediction, inference or a combination of both. For a particular case of quantitative responses, regularization techniques in regression are developed to overcome the weaknesses of ordinary least square (OLS) regression in prediction. These new shrinkage methods outperform OLS for prediction, especially in large datasets. In this study, a large dataset of field experiments on winter oilseed rape in Denmark for 22 years (1992 to 2013) was collected. Biweekly climatic data along with sowing date, harvest date, soil type and previous crop are considered as the explanatory variables. Yield of winter oilseed rape is considered as response variable. LASSO and Elastic Nets are the regularization techniques used to estimate the functions. Hold-one-out cross validation method for testing the prediction power reveals that these techniques are much useful in both prediction and inference. Since these techniques are included in recent versions of some software packages (e.g. R), they can be easily implemented by users at any level. The estimated function (model) is further used to predict the oilseed rape yield responses to climate change for several scenarios using representative weather data produced by a weather generator. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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MA @ admin @ |
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5129 |
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Shechter, M. |
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Assessing The Impact Of Climate Change On Agriculture And A Water Economy With A Diverse Mix Of Water Types – The Israeli Case Study |
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Conference Article |
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2014 |
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TradeM |
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World Congress of Environmental and Resource Economists 2014- WCERE2014, Istanbul, Turkey, 2014-06-28 to 2014-07-02 |
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MA @ admin @ |
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2829 |
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Shrestha, S., B Vosough Ahmadi, S Thomson and A Barnes |
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An assessment of the post 2015 CAP reforms: winners and losers in Scottish farming |
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2014 |
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88th Annual Conference, Paris, France, 2014-04-09 to 2014-04-11 |
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no |
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MA @ admin @ |
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2830 |
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Shrestha, S.; Hennessy, T.; Abdalla, M.; Forristal, D.; Jones, M.J. |
| Title |
Determining short term responses of Irish dairy farms under climate change |
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Journal Article |
| Year |
2014 |
Publication |
German Journal of Agricultural Economics |
Abbreviated Journal |
German Journal of Agricultural Economics |
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63 |
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3 |
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143-155 |
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This study aimed to determine short term farm responses of Irish dairy farms under climate change. The Irish National Farm Survey data and Irish weather data were the main datasets used in this study. A set of simulation models were used to determine grass yields and field time under a baseline scenario and a future climate scenario. An optimising farm level model which maximises farm net income under limiting farm resources was then run under these scenarios. Changes in farm net incomes under the climate change scenario compared to the baseline scenario were taken as a measure to determine the effect of climate change on farms. Any changes in farm activities under the climate run compared to the baseline run were considered as farm’s responses to maximise farm profits. The results showed that there was a substantial increase in yields of grass (49% to 56%) in all regions. The impact of climate change on farms was different based on the regions. Dairy farms in the Border, Midlands and South East regions suffered whereas dairy farms in other regions generally fared better under the climate change scenario. For a majority of farms, a substitution of concentrate feed with grass based feeds and increasing stocking rate were identified as the most common farm responses. However, farms replaced concentrate feed at varying degree. Dairy farms in the Mid East showed a move towards beef production system where medium dairy farms in the South East regions shifted entire tillage land to grass land. Farms in the South East region also kept animals on grass longer under the climate change scenario compared to the baseline scenario. |
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English |
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TradeM |
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no |
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MA @ admin @ |
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4672 |
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