Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Assessing the impact of the 'Decoupling' reform of the common agricultural policy on Irish farmers' off-farm labour market participation decisions

This paper assesses the impact of the 'decoupling' reform of the Common Agricultural Policy on the labour allocation decisions of Irish farmers. The agricultural household decision-making model provides the conceptual and theoretical framework to examine the interaction between government subsidies and farmers' time allocation decisions. The relationship postulated is that 'decoupling' of agricultural support from production would probably result in a decline in the return to farm labour but it would also lead to an increase in household wealth. The effect of these factors on how farmers allocate their time is tested empirically using labour participation and labour supply models. The models developed are sufficiently general for application elsewhere. The main findings for the Irish situation are that the decoupling of direct payments is likely to increase the probability of farmers participating in the off-farm employment market and that the amount of time allocated to off-farm work will increase.

Seeds of growth? Agricultural productivity and the transitional dynamics of the Ramsey model

A two-sector Ramsey-type model of growth is developed to investigate the relationship between agricultural productivity and economy-wide growth. The framework takes into account the peculiarities of agriculture both in production ( reliance on a fixed natural resource base) and in consumption (life-sustaining role and low income elasticity of food demand). The transitional dynamics of the model establish that when preferences respect Engel's law, the level and growth rate of agricultural productivity influence the speed of capital accumulation. A calibration exercise shows that a small difference in agricultural productivity has drastic implications for the rate and pattern of growth of the economy. Hence, low agricultural productivity can form a bottleneck limiting growth, because high food prices result in a low saving rate.

Bridging the gaps between agricultural policy, land-use and biodiversity

The fate of biodiversity is intimately linked to agricultural development. Policy reform is an important driver of changes in agricultural land-use, but there is considerable spatial variation in response to policy and its potential impact on biodiversity. We review the links between policy, land-use and biodiversity and advocate a more integrated approach. Ecologists need to recognize that wildlife-friendly farming is not the only land-use strategy that can be used to conserve biodiversity and to research alternative options such as land sparing. There is also a need for social scientists and ecologists to bring their approaches together, so that land-use change and its consequences can be investigated in a more holistic way.

Environmental impact of genetically modified cotton in South Africa

This paper presents the results of a large-scale study designed to monitor the impact arising from the introduction of insect-resistant Bt cotton in the Makhathini Flats, Republic of South Africa. Bt cotton provides a degree of resistance to cotton bollworm complex (Lepidoptera). Data were collected on the use of insecticides (type and quantity) as well as the farm-level economics of production from over 2200 farmers in three growing seasons (1998/1999, 1999/2000 and 2000/2001). and the results are discussed within the context of environmental impact brought about by insecticide. Over the three seasons of the study it was clear that Bt cotton provided benefits in terms of higher yield and gross margin relative to farmers growing conventional (non-Bt) cotton, and the benefits were particularly apparent for the smallest producers. Bt growers also used significantly less insecticide than growers of non-Bt cotton. Once quantities of insecticide applied to Bt and non-Bt cotton were converted into a Biocide Index and an Environmental Impact Quotient (EIQ) in order to allow for differences in terms of toxicity and persistence in the environment, it was apparent that the growing of Bt had a less negative impact on the environment. While this points to beneficial impacts on agricultural sustainability there are wider concerns regarding the vulnerability of resource-poor farmers in an area with limited (as yet) marketing options for their product and options for livelihood diversification both within and outside agriculture. Cotton producers in Makhathini are vulnerable as they rely on just One company for inputs (including, credit) and for their market. While Bt cotton provides benefits it does not in itself address some of the structural limitations that farmers face. (c) 2006 Elsevier B.V. All rights reserved.