Advances in application of climate prediction in agriculture

Hammer, Graeme; Hansen, James; Phillips, Jennifer; Mjelde, James W.; Hill, Harvey; Love, Alan; Potgieter, Andries

doi:10.1016/s0308-521x(01)00058-0

Cited by 182 publications

(125 citation statements)

References 49 publications

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“…The Probability of exceeding the long-term median (PEX) has been used in a number of agricultural and climatic forecasting systems (Stone and Auliciems, 1992;Meinke et al, 1996;Meinke and Hammer, 1997;Hammer et al, 2001) as a means to express the likelihood of a certain event. A transformed version of PEX (Equation (7)) was included as another quality measure in this study.…”

Section: Probability Of Exceeding the Medianmentioning

confidence: 99%

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Potgieter¹,

Everingham

Hammer

2003

Intl Journal of Climatology

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Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts.

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Section: Probability Of Exceeding the Medianmentioning

confidence: 99%

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Potgieter¹,

Everingham

Hammer

2003

Intl Journal of Climatology

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“…These seasonal predictions usually provide information on the probability of the starting and ending dates of the rainy season, the length of the season, the number of rainy days, the annual cumulative rainfall, the average and maximum duration of dry spells during the rainy season (Traore et al, 2014;Goddard et al, 2010). Various studies showed that climate information are integrated in farmers' decisions especially in the United States (Carberry et al, 2002), Mexico (Adams et al, 2003), Argentina (Podesta et al, 2002), Lesotho (Ziervogel, Bithell, & Washington, 2005), Australia (Hammer et al, 2001), Data were collected using a structured questionnaire referring to farmers' socio-economic characteristics and planted crop (Table 1). The data collection was also related to farmers' endogenous seasonal forecasts, their perception of climate information and WTP it.…”

Section: Introductionmentioning

confidence: 99%

Farmers’ Perception and Willingness to Pay for Climate Information in Burkina Faso

Zongo¹,

Diarra²,

Barbier³

et al. 2015

JAS

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This study highlights the perception and the option value of climate information in the sahelian and sudano-sahelian agro-climatic zones of Burkina Faso. From the sampled farmers only 21.78% have access to climate information, while 93% of them need climate information to guide through their decision in planning agricultural activities. Option value shows the interest granted by farmers using climate information. About 64% of the farmers are willing to pay an average of Francs CFA 546.34 to obtain climate information. The willingness to pay is determined by the the farmers' ability to predict the climate, to use radio as a means of information, farmers' awareness of previous forecasts and early onset of rainy season. While farmers considered benefits from the use of climate information, it is clear that its contribution to farm income remains a field of research to explore. Thus it is necessary to experiment with individual farms and to evaluate the contribution of climate information to the value added in different crops and farmers' income.

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“…By 'perfect' forecasts, we assume we know what is going to happen in the coming season at any time. The present forecasting skill is imperfect, and the knowledge to modify actions ahead of likely impacts is a limitation of applying climate predictions to improve agricultural systems (Hammer et al, 2001). So far, increasing efforts have been made on how to increase the value of the currently available forecasts, compared to no forecasts, by adjusting management practices and improving the climate-forecasting skill.…”

Section: Q Yu Et Almentioning

confidence: 99%

A modelling investigation into the economic and environmental values of ‘perfect’ climate forecasts for wheat production under contrasting rainfall conditions

Wang

Smith

2007

Intl Journal of Climatology

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With increased investment in improving climate forecasting techniques, it is essential to find ways of quantifying the maximum benefit of climate forecasts for a given industry. This paper describes an approach to quantify the value of 'perfect' climate forecasts to direct nitrogen management in a wheat-cropping system at two Australian locations with contrasting annual rainfall. For annual wheat-cropping systems, and compared with the N management based on optimal N rate derived from long-term climatic conditions, N management based on 'perfect' climate forecasts can lead to an average benefit of $65.2/ha/year at Walbundrie (annual rainfall 560.0 mm) and $66.5/ha/year at Wanbi (annual rainfall 314.5 mm). Generally, the economic benefit is highest in extreme (wet and dry) years and lowest in normal years. At the high rainfall site Walbundrie, where average N-application rate is high, the maximum yearly benefit was from significant saving through reduction in N application in driest years. At the low rainfall site Wanbi, where average N rates are low, the highest benefit was from both yield increases in the wettest years and saving of management and fertilizer cost in the driest years. Such optimized nitrogen management has little impact on excess drainage, but it can have significant impact on reduction of excess nitrogen, especially in high rainfall areas. An excess N reduction of 1314 kg N/ha at Wanbi and 1538 kg N/ha at Walbundrie can be achieved in 114 years. The significant reduction in N excess at Walbundrie may have profound environmental implications.

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Advances in application of climate prediction in agriculture

Cited by 182 publications

References 49 publications

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Farmers’ Perception and Willingness to Pay for Climate Information in Burkina Faso

A modelling investigation into the economic and environmental values of ‘perfect’ climate forecasts for wheat production under contrasting rainfall conditions

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