The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability

1996 ◽  
Vol 47 (5) ◽  
pp. 717 ◽  
Author(s):  
GL Hammer ◽  
DP Holzworth ◽  
R Stone
Keyword(s):  
Crop Management ◽  
Seasonal Forecast ◽  
Seasonal Forecasting ◽  
Wheat Crop ◽  
Phase System ◽  
Management Decisions ◽  
Seasonal Climate ◽  
Climate Forecasting ◽  
Daily Weather Data ◽  
Forecast Quality

In Australia, and particularly in the northern part of the grain belt, wheat is grown in an extremely variable climate. The wheat crop manager in this region is faced with complex decisions on choice of planting time, varietal development pattern, and fertiliser strategy. A skilful seasonal forecast would provide an opportunity for the manager to tailor crop management decisions more appropriately to the season. Recent developments in climate research have led to the development of a number of seasonal climate forecasting systems. The objectives of this study were to determine the value of the capability in seasonal forecasting to wheat crop management, to compare the value of the existing forecast methodologies, and to consider the potential value of improved forecast quality. We examined decisions on nitrogen (N) fertiliser and cultivar maturity using simulation analyses of specific production scenarios at a representative location (Goondiwindi) using long-term daily weather data (1894-1989). The average profit and risk of making a loss were calculated for the possible range of fixed (i.e. the same every year) and tactical (i.e. varying depending on seasonal forecast) strategies. Significant increase in profit (up to 20%) and/or reduction in risk (up to 35%) were associated with tactical adjustment of crop management of N fertiliser or cultivar maturity. The forecasting system giving greatest value was the Southern Oscillation Index (SOI) phase system of Stone and Auliciems (1992), which classifies seasons into 5 phases depending on the value and rate of change in the SOI. The significant skill in this system for forecasting both seasonal rainfall and frost timing generated the value found in tactical management of N fertiliser and cultivar maturity. Possible impediments to adoption of tactical management, associated with uncertainties in forecasting individual years, are discussed. The scope for improving forecast quality and the means to achieve it are considered by comparing the value of tactical management based on SO1 phases with the outcome given perfect prior knowledge of the season. While the analyses presented considered only one decision at a time, used specific scenarios, and made a number of simplifying assumptions, they have demonstrated that the current skill in seasonal forecasting is sufficient to justify use in tactical management of crops. More comprehensive studies to examine sensitivities to location, antecedent conditions, and price structure, and to assumptions made in this analysis, are now warranted. We have examined decisions related only to management of wheat. It would be appropriate to pursue similar analyses in relation to management decisions for other crops, cropping sequences, and the whole farm enterprise mix.

scholarly journals REVIEW OF SEASONAL CLIMATE FORECASTING FOR AGRICULTURE IN SUB-SAHARAN AFRICA

2011 ◽  
Vol 47 (2) ◽  
pp. 205-240 ◽  
Author(s):  
JAMES W. HANSEN ◽  
SIMON J. MASON ◽  
LIQIANG SUN ◽  
ARAME TALL
Keyword(s):  
Smallholder Farmers ◽  
Seasonal Forecast ◽  
Sub Saharan Africa ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Seasonal Climate ◽  
Climate Forecasting ◽  
Probabilistic Forecasts ◽  
Sub Saharan

SUMMARYWe review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

scholarly journals Forecasting rainfall based on the Southern Oscillation Index phases at longer lead-times in Australia

2013 ◽  
Vol 35 (4) ◽  
pp. 373 ◽  
Author(s):  
David H. Cobon ◽  
Nathan R. Toombs
Keyword(s):  
Key Management ◽  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Warm Season ◽  
Lead Times ◽  
Phase System ◽  
Management Decisions ◽  
Operational Forecasts ◽  
Eastern Australia ◽  
Forecast Quality

Under the extensive grazing conditions experienced in Australia, pastoralists would benefit from a long lead-time seasonal forecast issued for the austral warm season (November–March). Currently operational forecasts are issued publicly for rolling 3-month periods at lead-times of 0 or 1 month, usually without an indication of forecast quality. The short lag between the predictor and predictand limits use of forecasts because pastoralists operating large properties have insufficient time to implement key management decisions. The ability to forecast rainfall based on the Southern Oscillation Index (SOI) phase system was examined at 0–5-month lead-times for Australian rainfall. The SOI phase system provided a shift of adequate magnitude in the rainfall probabilities (–40 to +30%) and forecast quality for the 5-month austral warm season at lead-times >0 months. When data used to build the forecast system were used in verification, >20% of locations had a significant linear error in probability space (LEPS) and Kruskal–Wallis (KW) test for lead-times of 0–2 months. The majority of locations showing forecast quality were in northern Australia (north of 25°S), predominately in north-eastern Australia (north of 25°S, east of 140°E). Pastoralists in these areas can now apply key management decisions with more confidence up to 2 months before the November–March period. Useful lead-times of ≥3 months were not found.

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

1997 ◽  
Vol 54 (spe) ◽  
pp. 121-129 ◽  
Author(s):  
H. Meinke ◽  
R.C. Stone
Keyword(s):  
Southern Oscillation ◽  
Cropping Systems ◽  
Crop Management ◽  
Simulation Modelling ◽  
Wheat Crop ◽  
Phase System ◽  
Management Options ◽  
Oscillation Phenomenon ◽  
Climate Forecasts ◽  
Systems Simulation

The El Niño/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.

scholarly journals Use of the dry-spell seasonal forecast in crop management decisions

2021 ◽  
Vol 648 (1) ◽  
pp. 012092
Author(s):  
E Surmaini ◽  
E Susanti ◽  
Suciantini ◽  
M R Syahputra ◽  
F R Fajary
Keyword(s):  
Crop Management ◽  
Seasonal Forecast ◽  
Management Decisions ◽  
Dry Spell

scholarly journals Simulation of consumer exposure to deoxynivalenol according to wheat crop management and grain segregation: Case studies and methodological considerations

2005 ◽  
Vol 42 (3) ◽  
pp. 253-259 ◽  
Author(s):  
Marianne Le Bail ◽  
Philippe Verger ◽  
Thierry Doré ◽  
Jean-François Fourbet ◽  
Agnès Champeil ◽  
...  
Keyword(s):  
Case Studies ◽  
Crop Management ◽  
Wheat Crop ◽  
Methodological Considerations

scholarly journals Potential of seasonal hydrological forecasting of monthly run-off volumes for the Rhone and Arve Rivers from April to July

2018 ◽  
Author(s):  
Oriane Etter ◽  
Frédéric Jordan ◽  
Anton J. Schleiss
Keyword(s):  
Confidence Interval ◽  
Seasonal Forecast ◽  
Seasonal Forecasting ◽  
Average Error ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Rhône River ◽  
Run Off ◽  
Rhone River ◽  
The Difference

Abstract. In a context where water management is becoming increasingly important, reliable seasonal forecasting of discharge in rivers is crucial for making decisions several months in advance. This paper explores the potential of seasonal forecasting of run-off volumes produced by ensemble streamflow forecasting using past climatology and comparing it to the more commonly used average of past discharge measurements. The seasonal forecast was obtained for the Arve and Rhone rivers by simulation using the Routing System model for lead times of 30, 90 and 120 days. The initialization was performed on a validated simulation of 12 and 16 years for the Arve and Rhone rivers, respectively, obtained through long-term calibration. The performance was assessed by indicators called accuracy and thinness. The normalized mean average error (NMAE) was used to compare the performance of the seasonal forecast with the average of the past measurements. After a bias correction of the seasonal forecast of the Rhone River with the observed run-off volumes during the different lead times, the correlation of the median forecast with the measurements (accuracy) was larger than 0.55 for all lead times from April to July. The Arve River's accuracy was improved by disregarding the year 2007 member, leading to the floods of the 3rd and 9th of July, for lead times of 90 and 120 days. This resulting in the period of April to July having correlation accuracies higher than 0.5. For both rivers, the 80 % confidence interval of the seasonal forecast was relatively thin compared to the measurements (thinness) for the months of April to July. The NMAE was used to validate the range of validity of the forecast. The correction of the forecast resulted in more months being favorable for seasonal forecasting for the Rhone River. The post-processing on the Arve River decreased the difference between the measurement and the forecast (NMAE). Further investigation should concentrate on dividing the meteorological datasets to produce a strong median forecast and confidence interval

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