On tactical crop management using seasonal climate forecasts and simulation modelling: a case study for wheat

Abstract

ABSTRACT:The El Nino/Southern Oscillation phenomenon strongly influences rainfall distribution around the world. Using phases of the Southern Oscillation Index (SOI) allows a probabilistic forecast of future rainfall that can be useful to managers of agricultural systems. Using wheat as an example, we show in this study how the SOI phase system, when combined with a cropping systems simulation capability, can be used operationally to Improve tactical crop management and hence increase farm profits and/or decrease production risks. We show the validity of the approach for two contrasting locations, namely Dalby in Northern Australian and Piracicaba in Brazil At Dalby, highest median yields were achieved following a rapidly rising SOI phase in April/May and lowest median yields following a consistently negative phase. Conversely, highest median yields at Piracicaba followed a near zero April/May phase and lowest median yields when the phase was consistently positive. We show how tactical management options can range from crop or cultivar choice to nitrogen management and marketing of the future wheat crop. Key Words: climatic variability, crop and systems modelling, crop production forecasts PROCEDIMENTO DE MANEJO CULTURAL UTILIZANDO PREVISÃO CLIMÁTICA E MODELOS DE SIMULAÇÃO -UM ESTUDO DE CASO PARA A CULTURA DE TRIGORESUMO: O fenômeno El Niño/Oscilação Sul influencia marcadamente a distribuição de chuvas no mundo. O uso de fases de índice de Oscilação Sul (IOS) permite uma previsão probabilística da precipitação pluvial futura, que pode ser útil para o manejo de sistemas agrícolas. Usando-se trigo como exemplo, foi demonstrado nesse estudo, como o sistema de fases IOS, quando combinado com modelos de simulação de sistemas de cultivo, pode ser usado para melhorar o manejo tático de culturas e assim aumentar os lucros e/ou diminuir os riscos da produção agrícola. A validade desse método é mostrada para dois locais diferentes, Dalby, na Austrália e Piracicaba, no Brasil. Em Dalby, os maiores rendimentos médios foram obtidos após um aumento rápido na fase IOS em abril/maio e os menores rendimentos médios após uma fase negativa consistente. Em Piracicaba, os maiores rendimentos médios foram alcançados após uma fase próxima a zero em abril/maio e os menores rendimentos médios, quando a fase foi consistentemente positiva. É mostrado como as opções de manejo tático podem variar da escolha de cultivares ao manejo de nitrogênio e de mercado da cultura de trigo. Descritores: variabilidade climática, modelagem de cultura e sistemas, previsões de produção agrícola BACKGROUND

Use of climate information in soybean farming on the Argentinean pampas

“…Meinke & Stone 1997, Hammer et al 2001, Patt & Gwata 2002, However, results from southeastern South America are interesting in their own right because this region differs from locations such as Africa or Australia, where most of the studies on use of climate forecasts in agriculture have been focused. The ENSO signal that provides most of the seasonal climate predictability in the pampas region is less marked than in Australia.…”

“…It is often expected that seasonal climate forecasts should allow farmers to make proactive management decision, mitigating adverse conditions or, alternatively, taking advantage of favorable environments. Indeed, studies in different agricultural systems around the world have found potential benefits from the incorporation of climate forecast into the decision-making process (Meinke & Stone 1997, Mjelde et al 1999, Jones et al 2000, Podestá et al 2002. Despite the apparent benefits of seasonal climate forecasts, adoption of this technology has occurred more slowly and in a more haphazard way that was envisaged (Stern & Easterling 1999, Phillips et al 2001, Meinke & Stone 2005.…”

Modelling farming systems performance at catchment and regional scales to support natural resource management

“…At such scales, climate and soil data from one specific point are often assumed as representative of the study area and are used in the simulation, i.e., spatial heterogeneity is ignored. Well-validated models have been frequently used for the evaluation of alternative farm management systems [3][4][5][6] and scenario analyses, especially those linked to climate risk [7][8][9][10] and the impacts of future climate change [11][12][13][14]. The importance of spatial variations in climate, soil, and management systems is often assessed by conducting multiple point-scale simulations with combinations of those biophysical drivers.…”

A Two-Tier Statistical Forecast Method for Agricultural and Resource Management Simulations

“…Although SOI phase systems have been used to predict frost risk (Stone et al 1996b), in most applications their skill is based on the persistence of ENSO indicators between the preplanting and growing season period and those indicators' correlation with growing-season precipitation. Although many have conducted management simulations based on this simple forecast method (e.g., Meinke et al 1996;Meinke and Stone 1997;Carberry et al 2000;Stephens et al 2000;Hammer 2000;Hill et al 2000;Everingham et al 2003), such simulations can provide basic guidelines to the use of seasonal forecast information in agriculture. By testing for and defining best planting strategies they can determine management options that producers can consider in responding to forecast information.…”