Seasonal Climate Forecasts – Potential Agricultural-Risk Management Tools?

The importance of anchoring seasonal climate forecasts to user needs is examined in this paper. Although it is generally accepted that seasonal climate forecasts have potential value, many constraints preclude the optimal use of these forecasts, including the way forecasts are produced, interpreted and applied in a variety of decision-making processes. In South Africa, a variety of agricultural users exists, ranging from the small-scale farmer to larger commercial farming entities. Useful seasonal are those produced and disseminated with the end user in mind. A retroactive test period during the 1990s, evaluates the perceived impact of incorporating seasonal rainfall forecasts into decisions made by commercial crop farmers in the central parts of South Africa. Although a small sample of commercial farmers was interviewed, the results show some benefits to commercial agriculture if seasonal climate forecast information is continuously and effectively applied over the long-term.     

Targeting Climate Forecasts for Agricultural Applications in Sub-Saharan Africa: Situating Farmers in User-Space

Several meteorological services in Africa now issue seasonal climate forecasts on an operational basis. However, the failure to develop a comprehensive profile of users has resulted in a considerable gap between the information that is likely to be useful to farmers and that provided and disseminated by these services. The present study develops a methodology to characterize smallholder production systems in order to identify farmer groups who may adopt and benefit from the climate forecast information in sub-Saharan Africa. Through an extensive literature review, data and information was derived from a national household survey of 1540 smallholders in 1995–1997 by the Kenya Agricultural Research Institute and spatial georeferenced data from leading world data centers. The data were analysed and synthesized using the GIS. Considerable opportunities exist for farming communities to improve their profitability using climate forecasts. Although the needs and demand for climate forecasts vary according to the production systems and market forces that determine credit, demand and input availability and, thus, the usability of forecasts depend on the characteristics of the farmers and their place in space. Based on production strategies and options available to farmers, three zones were identified grouping farmers with highly probable, probable and less probable potential of adopting climate forecasts to alter their production practices. Although a climate forecast may be useful to all farmers in the region considered, due to different options available to individual groups of farmers, however, the benefits derived from its use may not be equitable. Some of the options available to farmers in Kenya were considered in this study with a view to highlighting why some may benefit more than others. The methodology demonstrated here could be adopted for other parts of the world for: (1) selecting survey sites to determine the benefits of climate forecasts using farmers participatory rapid rural appraisals and simulation approach, and (2) target climate information where it would be most useful.     

Participatory modeling in dairy farm systems: a method for building consensual environmental sustainability using seasonal climate forecasts

Dairy farmers face increasing pressure to decrease environmental impact while remaining economically viable. Adaptation of farm management practices in response to seasonal climate forecasts may be one means of achieving these objectives. This paper describes the interactive and iterative process by which farmers, researchers, extension agents, regulatory agencies, and other stakeholders collaborated to create, calibrate, and validate the Dynamic North Florida Dairy Farm model (DyNoFlo), a whole-farm decision support system to decrease nitrogen leaching while maintaining profitability under variable climate conditions. Participatory modeling may enhance the creation of adoptable and adaptable user-friendly models that include environmental, economic and biophysical components. By providing farmers, policy makers, and other stakeholders with a more holistic view of current practices, common ground among them was more easily identified and collaboration was fostered. Farmer values included willingness to be good environmental stewards when they are profitable. The participatory research and development process enhanced understanding of and potential adaptation to seasonal climate variability conditioned to the El Niño Southern Oscillation (ENSO) phases in light of increasing environmental regulations and economic challenges. Adoption of the collaboratively-developed DyNoFlo is expected to be higher than usual because stakeholders feel greater ownership of the final product.     

How stable are Australian farmers’ climate change risk perceptions? New evidence of the feedback loop between risk perceptions and behaviour

The exact relationship between people’s climate change attitudes and behaviour is a topic that engages policy-makers and researchers worldwide. Do climate change attitudes influence behaviour or is it possible that behaviour can change attitudes? This study uses a unique repeated survey dataset of 275 farmers (irrigators) in the southern Murray-Darling Basin from 2010–11 to 2015–16, to explore the dynamic relationship between climate change risk perceptions and farm adaptation behaviour. Farmers who had an increased risk exposure (expressed through higher debt, larger irrigated areas, greater share of permanent crops, and located in areas with higher temperatures and less rainfall) were more likely to agree climate change posed a risk. Whilst farmers became more accepting towards climate change over the time-period, a significant percentage of these attitudes were unstable. One reason suggested for this instability is the presence of a feedback loop between risk perceptions and behaviour. Namely, new evidence was found that farmers who agreed climate change was a risk in 2010–11, were more likely to undertake farm production decisions to reduce that risk (e.g. changing crop mix, reducing irrigated area and consequently selling water entitlements) – which had the impact of negatively feeding back and reducing their stated climate change risk perceptions in 2015–16. Conversely, farmers who were originally deniers were more likely to undertake somewhat riskier farm production decisions (e.g. increasing water utilisation rates and irrigation areas) – which consequently had the impact of positively increasing their climate change risk perceptions in 2015–16.     

Smallholder Maize Production and Climatic Risk: A Case Study from Mexico

The article explores the strategies employed by smallholder farmers in Mexico to cope with the affects of climatic variability, and how seasonal climate forecasts may assist these farmers in mitigating climatic risk. Recognizing that the decisions of smallholder farmers are intricately tied to the political-economic circumstances in which they operate, the article discusses how agricultural policy in Mexico affects the vulnerability of small-scale producers and may inhibit their ability to use climatic forecasts to their advantage. The article first reviews the literature on smallholder adaptation in Mexico, and discusses briefly policy and institutional issues affecting adaptation at the farm-level. Using the case of small-scale maize producers in Tlaxcala, Mexico, as an illustration, the article then argues that political-economic uncertainty outweighs climatic variability as a determinant of the production strategies of small-scale producers. In these circumstances, the farmers are unlikely to use new seasonal climate forecasts.     

The Use of Seasonal Climate Forecasting in Policymaking: Lessons from Northeast Brazil

This article examines the use of seasonal climate forecasting in public and private efforts to mitigate the impacts of drought in Ceará, Northeast Brazil. Here, forecasts have been directed towards small scale, rainfed agriculturalists as well as state and local level policymakers in the areas of agriculture, water management, and emergency drought relief. In assessing possibilities and constraints of forecast application in Ceará, the present analysis takes into account three types of variables: (a)characteristics of the forecasts; (b) policymaking systems; and (c)institutional environments. We conclude that, on the one hand, several factors in the Ceará case have limited the effectiveness of seasonal climate forecast use. First, the current level of skill of the forecasts is inadequate for the needs of policy development and farmer decisionmaking. Second, forecast information application has been subject to distortion, misinterpretation and political manipulation. Third, focus on the forecast as a product until recently neglected to take into account end users' needs and decisionmaking behavior. On the other hand, climate forecasting has the potential to offer a dramatic opportunity for state and local level bureaucracies to embark on a path of proactive drought planning.     

AgClimate: a case study in participatory decision support system development

Potential economic benefit exists from the use of seasonal climate forecasts in agriculture. To assess potential end user attitudes toward and interests in climate data, and to provide inputs from users to the development of decision support tools, we conducted a series of surveys. Survey results affected the design, development, and enhancement of AgClimate, a web-based decision support system for minimizing climate risks to agriculture. The overall process is an example of how decision makers can participate in the research process, thereby improving the value and relevance of research products such as decision support systems.     

Understanding social resilience to climate variability in primary enterprises and industries

Resource-dependent industries are particularly vulnerable to climate change, and their ability to adapt will be as critical to society as to the natural systems upon which they rely. More than ever, resource-users will need to anticipate, and prepare for, climate-related changes, and institutions will need to be particularly supportive, if resource industries and the extended social systems dependent on them are to be sustained. I examine the capacity of cattle-graziers in Australia to cope and adapt to climate variability as a precursor for understanding their vulnerability to climate change by assessing: (i) their perception of risk, (ii) their capacity to plan, learn and reorganise, (iii) their proximity to the thresholds of coping, and (iv) their level of interest in adapting to change. Graziers perceived themselves to be resilient to climate variability in their perceptions of climate risk, reorganising capacity, coping, and interest in adapting. Their dependency on the grazing resource and use of seasonal climate forecasts were significant influences, suggesting that resilience could be enhanced. Facilitated collaborative learning amongst graziers and other stakeholders may assist to develop strategic skills, increasing climate awareness, developing financial security and adopt climate tools such as seasonal climate forecasts. Enhanced strategies for coping with climate variability will provide a way for encouraging gradual, incremental adjustments for climate adaptation.     

Farmer beliefs and concerns about climate change and attitudes toward adaptation and mitigation: Evidence from Iowa

Agriculture is both vulnerable to climate change impacts and a significant source of greenhouse gases. Increasing agriculture’s resilience and reducing its contribution to climate change are societal priorities. Survey data collected from Iowa farmers are analyzed to answer the related research questions: (1) do farmers support adaptation and mitigation actions, and (2) do beliefs and concerns about climate change influence those attitudes. Results indicate that farmers who were concerned about the impacts of climate change on agriculture and attributed it to human activities had more positive attitudes toward both adaptive and mitigative management strategies. Farmers who believed that climate change is not a problem because human ingenuity will enable adaptations and who did not believe climate change is occurring or believed it is a natural phenomenon—a substantial percentage of farmers—tended not to support mitigation.     

Seasonal climate prediction for South America with FSU Multi-model Synthetic Superensemble algorithm

Summary Objective combination schemes of predictions from different models have been applied to seasonal climate forecasts. These schemes are successful in producing a deterministic forecast superior to individual member models and better than the multi-model ensemble mean forecast. Recently, a variant of the conventional superensemble formulation was created to improve skills for seasonal climate forecasts, the Florida State University (FSU) Synthetic Superensemble. The idea of the synthetic algorithm is to generate a new data set from the predicted multimodel datasets for multiple linear regression. The synthetic data is created from the original dataset by finding a consistent spatial pattern between the observed analysis and the forecast data set. This procedure is a multiple linear regression problem in EOF space. The main contribution this paper is to discuss the feasibility of seasonal prediction based on the synthetic superensemble approach and to demonstrate that the use of this method in coupled models dataset can reduce the errors of seasonal climate forecasts over South America. In this study, a suite of FSU coupled atmospheric oceanic models was used. In evaluation the results from the FSU synthetic superensemble demonstrate greater skill for most of the variables tested here. The forecast produced by the proposed method out performs other conventional forecasts. These results suggest that the methodology and database employed are able to improve seasonal climate prediction over South America when compared to the use of single climate models or from the conventional ensemble averaging. The results show that anomalous conditions simulated over South America are reasonably realistic. The negative (positive) precipitation anomalies for the summer monsoon season of 1997/98 (2001/02) were predicted by Synthetic Superensemble formulation quite well. In summary, the forecast produced by the Synthetic Superensemble approach outperforms the other conventional forecasts.     

Beyond the “fit”: introducing climate forecasts among organic farmers in Georgia (United States)

Organic farmers are a prime clientele for climate services by virtue of their social profile and vulnerability of produce to climate extremes. The study draws on an online survey and in-depth interviews with organic farmers in Georgia (United States). It shows that organic farmers access and act on climate information in ways that reflect their emphasis on diversified and flexible systems. They favor a pluralistic knowledge base that integrates scientific expertise with place-based experience and intuitive understandings. Their management style combines information at multiple temporal scales and draws on a range of technical and social resources. Translating climate forecasts into usable science for organic farming requires attention to the identities, commitments, and relationships that define the organic farming community.     

From accessing to assessing forecasts: an end-to-end study of participatory climate forecast dissemination in Burkina Faso (West Africa)

This study compares responses to seasonal climate forecasts conducted by farmers of three agro-ecological zones of Burkina Faso, including some who had attended local level workshops and others who had not attended the workshops. While local inequalities and social tensions contributed to excluding some groups, about two-thirds of non-participants interviewed received the forecast from the participants or through various means deployed by the project. Interviews revealed that almost all those who received the forecasts by some mechanism (workshop or other) shared them with others. The data show that participants were more likely to understand the probabilistic aspect of the forecasts and their limitations, to use the information in making management decisions and by a wider range of responses. These differences are shown to be statistically significant. Farmers evaluated the forecasts as accurate and useful in terms of both material and non-material considerations. These findings support the hypothesis that participatory workshops can play a positive role in the provision of effective climate services to African rural producers. However, this role must be assessed in the context of local dynamics of power, which shape information flows and response options. Participation must also be understood beyond single events (such as workshops) and be grounded in sustained interaction and commitments among stakeholders. The conclusion of this study point to lessons learned and critical insights on the role of participation in climate-based decision support systems for rural African communities.     

Value of perfect ENSO phase predictions for agriculture: evaluating the impact of land tenure and decision objectives

In many places, predictions of regional climate variability associated with the El Niño–Southern Oscillation phenomenon offer the potential to improve farmers’ decision outcomes, by mitigating the negative impacts of adverse conditions or by taking advantage of favorable conditions. While the notion that climate forecasts are potentially valuable has been established, questions of when they may be more or less valuable have proven harder to resolve. Using simulations, we estimate the expected value of seasonal climate information under alternative assumptions about (a) land tenure (ownership vs. short-term leases) and (b) the decision maker’s objective function (expected utility vs. prospect theory value function maximization), employing a full range of plausible parameter values for each objective function. This allows us to show the extent to which the value of information depends on risk preferences, loss aversion, wealth levels and expectations, as well as situational constraints. Our results demonstrate in a non-laboratory decision context that, in some cases, psychologically plausible deviations from expected utility maximization can lead to substantial differences in estimates of the expected value of climate forecasts. Efforts to foster effective use of climate information and forecasts in agriculture must be grounded in a firm understanding of the goals, objectives and constraints of decision makers.     

Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites

The present article is a contribution to the CLARIS WorkPackage “Climate and Agriculture”, and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative of different climate and soil conditions in Argentina. Considering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the DSSAT (Decision Support System for Agrotechnology Transfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). We simulated yields for three different sowing dates for each crop in each of the three sites. Also considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. This type of information is used as input (predictand) for non-linear statistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosen sowing date. MARS models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. Classification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. In order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. Above normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. Using this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. More generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. However, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions.     

Climate change and the agricultural sector in Ireland: examining farmer awareness and willingness to adopt new advisory mitigation tools

Agriculture is responsible for the bulk of Ireland’s greenhouse gas (GHG) emissions. However, the potential to mitigate some of these emissions through the adoption of more efficient farm management practices may be hampered by farmers’ awareness and attitude towards climate change and agriculture’s role in contributing to GHG emissions. This paper presents results from a survey of 746 Irish farmers in 2014, with a view to understanding farmers’ awareness of, and attitudes to, climate change and GHG emissions. Survey results show that there was a general uncertainty towards a number of questions related to agricultural GHG emissions, e.g. if tilling of land causes GHG emissions, and that farmers were reluctant to take action to reduce GHG emissions on their farm. To further explore farmers’ attitudes towards climate change, a multinomial logit model was used to examine the socio-economic factors that affect farmers’ willingness to adopt an advisory tool that would show the potential reduction in GHG emissions from the adoption of new technologies. Results show that farmers’ awareness of human-induced global climate change was positively related to the tool’s adoption.

Key policy insights

• Irish farmers are generally not sufficiently aware of the impact of their activities on climate change.

• A quarter of farmers believed that climate change will only impact on their business in the long-term; such an attitude may lead to a reluctance amongst these farmers to adopt management practices that reduce GHG emissions.

• Awareness of climate change affects positively the adoption of new tools to reduce GHG emissions on farmers’ farms.

• IT literacy affects willingness to adopt new tools to address GHG emissions.

• Reception of agri-environmental advice can have a positive influence on farmers’ willingness to adopt new GHG emission abatement tools.

• Farmers in receipt of environmental subsidies are more likely to adopt new abatement tools, either because they are more environmentally conscious or because the subsidy raised their environmentally consciousness.

• Willingness to adopt differs between different farm enterprises; operating dairy enterprise increases the willingness to adopt new advisory mitigation tools.

     

A typology of dairy farmer perceptions towards climate change

Dairy farming is an industry which could potentially mitigate a large amount of greenhouse gas emissions. However, perception and acceptance towards climate change is a significant barrier to voluntary adoption of best practice techniques. A number of countries have set targets for reducing emissions, of which Scotland has one of the most ambitious agendas. This paper presents results from an extensive survey of 540 dairy farmers, conducted in 2009, with the aim of understanding attitudes, values and intentions towards climate change. Only half of these farmers agreed that temperatures would rise in the future and this could significantly hinder adoption of voluntary measures to meet emissions targets. To explore this further a typology was developed on the responses to attitude and value statements, using principal components and cluster analysis methods. Six distinct types were found to exist which had a range of outlooks towards the impact of climate change in the future. However, five of the six types stated no intention to adopt practices which would reduce emissions. The typology approach supports diversified engagement strategies and a more innovation-led or resource maximisation view towards farming was expressed by several of these types. This may indicate that policy makers should focus on ‘win-win’ technologies as a means to effectively engage with these. However, a number of types were disengaged from the process which was driven by uncertainties towards projections for global warming and this needs to be addressed by both scientists and policy makers to ensure greater participation within the farming community.     

The Uncertain Value of Perfect ENSO Phase Forecasts: Stochastic Agricultural Prices and Intra-Phase Climatic Variations

Climate and crop yield variability associated with El Niño—Southern Oscillation (ENSO) are now predictable within limits. This predictability suggests a potential to tailor agricultural management to mitigate impacts of adverse conditions and to take advantage of favorable conditions. However, improved climate predictions may benefit society only with parallel advances in our ability to use this knowledge. We show that the value that will accrue to any given actor from an ENSO phase forecast should be viewed not as a known number but instead as a random draw from a distribution, even when the forecast is always correct. Forecast value depends on the highly variable contexts in which forecasts are used. Randomness in forecast value has significant implications for choices made by forecasters, forecast users and policy makers. To show randomness, we estimate potential economic values of ENSO forecasts for agricultural producers based on two realistic assumptions: the crop prices farmers receive are uncertain; and within an ENSO phase, the actual climate is variable in ways that affect profits. The use of synthetic weather and crop price series, with crop simulation models, helps show the range and likelihood of climate forecast value.     

Calibration and combination of dynamical seasonal forecasts to enhance the value of predicted probabilities for managing risk

Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2–4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability.     

Assessing the spatial impact of climate on wheat productivity and the potential value of climate forecasts at a regional level

Quantification of the spatial impact of climate on crop productivity and the potential value of seasonal climate forecasts can effectively assist the strategic planning of crop layout and help to understand to what extent climate risk can be managed through responsive management strategies at a regional level. A simulation study was carried out to assess the climate impact on the performance of a dryland wheat-fallow system and the potential value of seasonal climate forecasts in nitrogen management in the Murray-Darling Basin (MDB) of Australia. Daily climate data (1889–2002) from 57 stations were used with the agricultural systems simulator (APSIM) to simulate wheat productivity and nitrogen requirement as affected by climate. On a good soil, simulated grain yield ranged from <2 t/ha in west inland to >7 t/ha in the east border regions. Optimal nitrogen rates ranged from <60 kgN/ha/yr to >200 kgN/ha/yr. Simulated gross margin was in the range of –$20/ha to $700/ha, increasing eastwards. Wheat yield was closely related to rainfall in the growing season and the stored soil moisture at sowing time. The impact of stored soil moisture increased from southwest to northeast. Simulated annual deep drainage ranged from zero in western inland to >200 mm in the east. Nitrogen management, optimised based on ‘perfect’ knowledge of daily weather in the coming season, could add value of $26～$79/ha compared to management optimised based on historical climate, with the maximum occurring in central to western part of MDB. It would also reduce the nitrogen application by 5～25 kgN/ha in the main cropping areas. Comparison of simulation results with the current land use mapping in MDB revealed that the western boundary of the current cropping zone approximated the isolines of 160 mm of growing season rainfall, 2.5t/ha of wheat grain yield, and $150/ha of gross margin in QLD and NSW. In VIC and SA, the 160-mm isohyets corresponded relatively lower simulated yield due to less stored soil water. Impacts of other factors like soil types were also discussed.