scholarly journals Economic Value of Seasonal Climate Forecasts for Agriculture: Review of Ex-Ante Assessments and Recommendations for Future Research

2008 ◽  
Vol 47 (5) ◽  
pp. 1269-1286 ◽  
Author(s):  
Francisco J. Meza ◽  
James W. Hansen ◽  
Daniel Osgood

Abstract Advanced information in the form of seasonal climate forecasts has the potential to improve farmers’ decision making, leading to increases in farm profits. Interdisciplinary initiatives seeking to understand and exploit the potential benefits of seasonal forecasts for agriculture have produced a number of quantitative ex-ante assessments of the economic value of seasonal climate forecasts. The realism, robustness, and credibility of such assessments become increasingly important as efforts shift from basic research toward applied research and implementation. This paper surveys published evidence about the economic value of seasonal climate forecasts for agriculture, characterizing the agricultural systems, approaches followed, and scales of analysis. The climate forecast valuation literature has contributed insights into the influence of forecast characteristics, risk attitudes, insurance, policy, and the scale of adoption on the value of forecasts. Key innovations in the more recent literature include explicit treatment of the uncertainty of forecast value estimates, incorporation of elicited management responses into bioeconomic modeling, and treatment of environmental impacts, in addition to financial outcomes of forecast response. It is argued that the picture of the value of seasonal forecasts for agriculture is still incomplete and often biased, in part because of significant gaps in published valuation research. Key gaps include sampling of a narrow range of farming systems and locations, incorporation of an overly restricted set of potential management responses, failure to consider forecast responses that could lead to “regime shifts,” and failure to incorporate state-of-the-art developments in seasonal forecasting. This paper concludes with six recommendations to enhance the realism, robustness, and credibility of ex-ante valuation of seasonal climate forecasts.

2020 ◽  
Vol 12 (1) ◽  
pp. 3-14 ◽  
Author(s):  
D. H. Cobon ◽  
R. Darbyshire ◽  
J. Crean ◽  
S. Kodur ◽  
M. Simpson ◽  
...  

AbstractSeasonal climate forecasts (SCFs) provide opportunities for pastoralists to align production decisions to climatic conditions, as SCFs offer economic value by increasing certainty about future climatic states at decision-making time. Insufficient evidence about the economic value of SCFs was identified as a major factor limiting adoption of SCFs in Australia and abroad. This study examines the value of SCFs to beef production system management in northern Australia by adopting a theoretical probabilistic climate forecast system. Stocking rate decisions in October, before the onset of the wet season, were identified by industry as a key climate sensitive decision. The analysis considered SCF value across economic drivers (steer price in October) and environmental drivers (October pasture availability). A range in forecast value was found ($0–$14 per head) dependent on pasture availability, beef price, and SCF skill. Skillful forecasts of future climate conditions offered little value with medium or high pasture availability, as in these circumstances pastures were rarely overutilized. In contrast, low pasture availability provided conditions for alternative optimal stocking rates and for SCFs to be valuable. Optimal stocking rates under low pasture availability varied the most with climate state (i.e., wet or dry), indicating that producers have more to gain from a skillful SCF at these times. Although the level of pasture availability in October was the major determinant of stocking rate decisions, beef price settings were also found to be important. This analysis provides insights into the potential value of SCFs to extensive beef enterprises and can be used by pastoralists to evaluate the cost benefit of using a SCF in annual management.


2020 ◽  
Vol 148 (1) ◽  
pp. 437-456 ◽  
Author(s):  
Andrew Schepen ◽  
Yvette Everingham ◽  
Quan J. Wang

Abstract Multivariate seasonal climate forecasts are increasingly required for quantitative modeling in support of natural resources management and agriculture. GCM forecasts typically require postprocessing to reduce biases and improve reliability; however, current seasonal postprocessing methods often ignore multivariate dependence. In low-dimensional settings, fully parametric methods may sufficiently model intervariable covariance. On the other hand, empirical ensemble reordering techniques can inject desired multivariate dependence in ensembles from template data after univariate postprocessing. To investigate the best approach for seasonal forecasting, this study develops and tests several strategies for calibrating seasonal GCM forecasts of rainfall, minimum temperature, and maximum temperature with intervariable dependence: 1) simultaneous calibration of multiple climate variables using the Bayesian joint probability modeling approach; 2) univariate BJP calibration coupled with an ensemble reordering method (the Schaake shuffle); and 3) transformation-based quantile mapping, which borrows intervariable dependence from the raw forecasts. Applied to Australian seasonal forecasts from the ECMWF System4 model, univariate calibration paired with empirical ensemble reordering performs best in terms of univariate and multivariate forecast verification metrics, including the energy and variogram scores. However, the performance of empirical ensemble reordering using the Schaake shuffle is influenced by the selection of historical data in constructing a dependence template. Direct multivariate calibration is the second-best method, with its far superior performance in in-sample testing vanishing in cross validation, likely because of insufficient data relative to the number of parameters. The continued development of multivariate forecast calibration methods will support the uptake of seasonal climate forecasts in complex application domains such as agriculture and hydrology.


2020 ◽  
Author(s):  
Tanja Portele ◽  
Christof Lorenz ◽  
Patrick Laux ◽  
Harald Kunstmann

<p>Semi-arid regions are the regions mostly affected by drought. In these climatically sensitive regions, the frequency and intensity of drought and hot extremes is projected to increase. With increasing precipitation variability in semi-arid regions, sustainable water management is required. Proactive drought and extreme event preparedness, as well as damage mitigation could be provided by the use of seasonal climate forecasts. However, their probabilistic nature, the lack of clear action derivations and institutional conservatism impedes their application in decision making of the water management sector. Using the latest global seasonal climate forecast product (SEAS5) at 35 km resolution and 7 months forecast horizon of the European Centre for Medium-Range Weather Forecasts, we show that seasonal-forecast-based actions offer potential economic benefit and allow for climate proofing in semi-arid regions in the case of drought and extreme events. Our analysis includes 7 semi-arid, in parts highly managed river basins with extents from tens of thousands to millions of square kilometers in Africa, Asia and South America. The value of the forecast-based action is derived from the skill measures of hit (worthy action) and false alarm (action in vain) rate and is related to economic expenses through ratios of associated costs and losses of an early action. For water management policies, forecast probability triggers for early action plans can be offered based on expense minimization and event maximization criteria. Our results show that even high lead times and long accumulation periods attain value for a range of users and cost-loss situations. For example, in the case of extreme wet conditions (monthly precipitation above 90<sup>th</sup> percentile), seasonal-forecast-based action in 5 out of 7 regions can still achieve more than 50 % of saved expenses of a perfect forecast at 6 months in advance. The utility of seasonal forecasts strongly depends on the user, the cost-loss situation, the region and the concrete application. In general, seasonal forecasts allow decision makers to save expenses, and to adapt to and mitigate damages of extreme events related to climate change.</p>


2011 ◽  
Vol 32 (5) ◽  
pp. 759-771 ◽  
Author(s):  
P. Roudier ◽  
B. Sultan ◽  
P. Quirion ◽  
C. Baron ◽  
A. Alhassane ◽  
...  

2019 ◽  
Vol 41 (3) ◽  
pp. 165
Author(s):  
Duc-Anh An-Vo ◽  
Kate Reardon-Smith ◽  
Shahbaz Mushtaq ◽  
David Cobon ◽  
Shreevatsa Kodur ◽  
...  

Seasonal climate forecasts (SCFs) have the potential to improve productivity and profitability in agricultural industries, but are often underutilised due to insufficient evidence of the economic value of forecasts and uncertainty about their reliability. In this study we developed a bio-economic model of forecast use, explicitly incorporating forecast uncertainty. Using agricultural systems (ag-systems) production simulation software calibrated with case study information, we simulated pasture growth, herd dynamics and annual economic returns under different climatic conditions. We then employed a regret and value function approach to quantify the potential economic value of using SCFs (at both current and improved accuracy levels) in decision making for a grazing enterprise in north-eastern Queensland, Australia – a region subject to significant seasonal and intra-decadal climate variability. Applying an expected utility economic modelling approach, we show that skilled SCF systems can contribute considerable value to farm level decision making. At the current SCF skill of 62% (derived by correlating the El Niño Southern Oscillation (ENSO) signal and historical climate data) at Charters Towers, an average annual forecast value of AU$4420 (4.25%) was realised for the case study average annual net profit of AU$104000, while a perfect (no regret) forecast system could result in an increased return of AU$13475 per annum (13% of the case study average annual net profit). Continued improvements in the skill and reliability of SCFs is likely to both increase the value of SCFs to agriculture and drive wider uptake of climate forecasts in on-farm decision making. We also anticipate that an integrated framework, such as that developed in this study, may provide a pathway for better communication with end users to support improved understanding and use of forecasts in agricultural decision making and enhanced sustainability of agricultural enterprises.


2002 ◽  
Vol 34 (3) ◽  
pp. 603-632 ◽  
Author(s):  
Harvey S.J. Hill ◽  
James W. Mjelde

Use of seasonal climate forecasts is a rapidly evolving area. Effective research and application of climate forecasts require close cooperation between scientists in diverse disciplines and decision makers. Successful collaboration requires all players to at least partially understand each other's perspectives. Issues associated with seasonal forecasts, through a selected review of both physical and social sciences literature, is presented. Our hope is that the review will improve research in this area by stimulating further collaborations.


2014 ◽  
Vol 11 (96) ◽  
pp. 20131162 ◽  
Author(s):  
A. Weisheimer ◽  
T. N. Palmer

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time.


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