Search

Abstract In agrometeorology and management of meteorology related natural resources, many traditional methods and indigenous technologies are still in use or being revived for managing low external inputs sustainable agriculture (LEISA) under conditions of climate variability. This paper starts with the introduction of an end-to-end climate information build up and transfer system in agrometeorology, in which the use of such methods and technologies must be seen to operate. It then reviews the options that LEISA farmers have in risk management of agrometeorological and agroclimatological calamities. This is based on the role that the pertinent meteorological/climatological parameters and phenomena play as limiting factors in agricultural production and the expectations on their variability. Subsequently, local case studies are given as examples of preparedness strategies to cope with i). variable water/moisture flows, including mechanical impacts of rain and/or hail, ii). variable temperature and heat flows, including fires, and iii). fitting cropping periods to the varying seasons, everywhere including related phenomena as appropriate. The paper ends with a series of important additional considerations without which the indicated strategies cannot be successful on a larger scale and in the long run. [ABSTRACT FROM AUTHOR]

The aim of this paper is to improve understanding of the adaptive capacity of European agriculture to climate change. Extensive data on farm characteristics of individual farms from the Farm Accountancy Data Network (FADN) have been combined with climatic and socio-economic data to analyze the influence of climate and management on crop yields and income and to identify factors that determine adaptive capacity. A multilevel analysis was performed to account for regional differences in the studied relationships. Our results suggest that socio-economic conditions and farmcharacteristics should be considered when analyzing effects of climate conditions on farm yields and income. Next to climate, input intensity, economic size and the type of land use were identified as important factors influencing spatial variability in crop yields and income. Generally, crop yields and income are increasing with farm size and farm intensity. However, effects differed among crops and high crop yields were not always related to high incomes, suggesting that impacts of climate and management differ by impact variable. As farm characteristics influence climate impacts on crop yields and income, they are good indicators of adaptive capacity at farm level and should be considered in impact assessment models. Different farm types with different management strategies will adapt differently. [ABSTRACT FROM AUTHOR]

Abstract PNNLs Agriculture and Land Use is used to demonstrate the impact of potential changes in climate on agricultural production and land use in the United States. AgLU simulates production of four crop types in several world regions, in 15-yr time steps from 1990 to 2095. Changes in yield of major field crops in the United States, for 12 climate scenarios, are obtained from simulations of the EPIC crop growth model. Results from the HUMUS model are used to constrain crop irrigation, and BIOME3 model is used to simulate productivity of unmanaged ecosystems. Assumptions about changes in agricultural productivity outside the United States are treated on a scenario basis, either responding in the same way as in the United States, or not responding to climate. [ABSTRACT FROM AUTHOR]

Abstract Climate variability and change affects individuals and societies. Within agricultural systems, seasonal climate forecasting can increase preparedness and lead to better social, economic and environmental outcomes. However, climate forecasting is not the panacea to all our problems in agriculture. Instead, it is one of many risk management tools that sometimes play an important role in decision-making. Understanding when, where and how to use this tool is a complex and multi-dimensional problem. To do this effectively, we suggest a participatory, cross-disciplinary research approach that brings together institutions (partnerships), disciplines (e.g., climate science, agricultural systems science, rural sociology and many other disciplines) and people (scientist, policy makers and direct beneficiaries) as equal partners to reap the benefits from climate knowledge. Climate science can provide insights into climatic processes, agricultural systems science can translate these insights into management options and rural sociology can help determine the options that are most feasible or desirable from a socio-economic perspective. Any scientific breakthroughs in climate forecasting capabilities are much more likely to have an immediate and positive impact if they are conducted and delivered within such a framework. While knowledge and understanding of the socio-economic circumstances is important and must be taken into account, the general approach of integrated systems science is generic and applicable in developed as well as in developing countries. Examples of decisions aided by simulation output ranges from tactical crop management options, commodity marketing to policy decisions about future land use. We also highlight the need to better understand temporal- and spatial-scale variability and argue that only a probabilistic approach to outcome dissemination should be considered. We demonstrated how knowledge of climatic variability (CV), can lead to better decisions in agriculture, regardless of geographical location and socio-economic conditions. [ABSTRACT FROM AUTHOR]

Abstract Agricultural meteorologists are concerned with many operational aspects of the effects of climate on crop production, livestock, and natural resource management. For them to continue to make a contribution to the economy of a country they must continually sharpen their skills and remain updated on the latest available information. Training should include a variety of skills, including transferable skills (e.g. communication, numeracy), professional skills (including cognitive skills) and information technology skills. Problem-based learning can be used to promote critical thinking, decision making and analytical skills. More use should be made of computer-aided learning for agricultural meteorologists in-service training. In particular, the Internet or CDs could be used to disseminate specific recently developed techniques and applications to improve the understanding of the variability in climate and its effect on agricultural production and natural resource management. Examples that can address the vulnerability of farmers include cropclimate matching, the use of indices, crop modelling and risk assessment together with seasonal outlooks. A strategy needs to be formulated to address these needs and implement changes in the education and training of agricultural meteorologists. These training needs must be constantly updated to meet the changing demands of new technology to cope with climate change and climate variability. [ABSTRACT FROM AUTHOR]