Climatic information and decision-making in maize crop production systems of the Argentinean Pampas

Bert, Federico; Satorre, Emilio H.; Toranzo, Fernando Ruiz; Podestá, Guillermo

doi:10.1016/j.agsy.2005.03.007

Cited by 53 publications

(34 citation statements)

References 51 publications

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“…The idea was to identify the different entry points in the farmers' decision process in which SCFs could be of use and the conditions that needed to be in place to allow them to use it in their decision-making processes (cf. Bert et al, 2006;Jones et al, 1998). This was achieved by asking farmers to identify the key farming decisions they would have to pursue in the following months (between Feb-Apr) and ask them to reflect on these decisions based on the 3-month outlooks provided.…”

Section: Second Stage: Winter Of 2015/2016mentioning

confidence: 99%

The land management tool: Developing a climate service in Southwest UK

Falloon

Soares

Manzanas³

et al. 2018

Climate Services

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A B S T R A C TSeasonal climate forecasts (SCFs) have significant potential to support shorter-term agricultural decisions and longer-term climate adaptation plans, but uptake in Europe has to date been low. Under the European Union funded project, European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales (EUPORIAS) we have developed the Land Management Tool (LMTool), a prototype seasonal climate service for land managers, working closely in collaboration with two stakeholder organizations, Clinton Devon Estates (CDE) and the National Farmers Union (NFU). LMTool was one of several prototype climate services selected for development within EUPORIAS, including those for the UK transport network, food security in Ethiopia, renewable energy production, hydroelectric energy production in Sweden, and river management in two French basins. The LMTool provides SCFs (1-3 months ahead) to farmers in the Southwest UK, alongside 14-day site specific weather forecasts during the winter months when the skill of seasonal forecasts is greatest.We describe the processes through which the LMTool was co-designed and developed with the farmers, its technical development and key features; critically examine the lessons learned and their implications for providing future climate services for land managers; and finally assess the feasibility of delivering an operational winter seasonal climate service for UK land managers.A number of key learning points from developing the prototype may benefit future work in climate services for the land management and agriculture sector; many of these points are also valid for climate services in other sectors. Prototype development strongly benefitted from; working with intermediaries to identify representative, engaged land managers; an iterative and flexible process of co-design with the farmer group; and from an interdisciplinary project team. Further work is needed to develop a better understanding of the role of forecast skill in land management decision making, the potential benefits of downscaling and how seasonal forecasts can help support land managers decision-making processes. The prototype would require considerable work to implement a robust operational forecast system, and a longer period to demonstrate the value of the services provided. Finally, the potential for such services to be applied more widely in Europe is not well understood and would require further stakeholder engagement and forecast development. Practical implicationsAs part of the EU project EUPORIAS (Buontempo and Hewitt, 2017), the UK Met Office, University of Leeds, Predictia and KNMI-in close collaboration with Clinton Devon Estates (CDE) and the National Farmers Union (NFU)-have developed the Land Management Tool (LMTool), a prototype http://dx

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Section: Second Stage: Winter Of 2015/2016mentioning

confidence: 99%

The land management tool: Developing a climate service in Southwest UK

Falloon

Soares

Manzanas³

et al. 2018

Climate Services

View full text Add to dashboard Cite

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“…Closing the gap between the availability of climate forecasts and their effective application depends, among other things, on a firm understanding of users' needs and decisionmaking processes, and of the broader context in which decisions are made (Stern & Easterling 1999, Eakin 2000, Roncoli et al 2001, Stewart et al 2004, Bert et al 2006. The best way to develop a realistic understanding of decisions and their context is to interact with actual or potential users of climate information (Hammer et al 2001, Stewart et al 2004.…”

Section: Abstract: Enso · Climate Forecasts · Decision Making · Descmentioning

confidence: 99%

Use of climate information in soybean farming on the Argentinean pampas

Bert¹,

Podestá

Satorre³

et al. 2007

Clim. Res.

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“…We focus on the region near Pergamino (33 • 56 S, 60 • 33 W), the most productive sub-region of the Pampas (Paruelo and Sala 1993). Earlier evidence of forecast value in attitudinal , agroclimatological (Ferreyra et al 2001;Podestá et al 2002) and decision studies Letson et al 2005;Bert et al 2006Bert et al , 2007Podestá et al 2008) suggested Pergamino as a promising region for study.…”

Section: Case Studymentioning

confidence: 95%

“…After decisions about land assignment are made, farmers focus on crop management decisions. Decisions related to crop management are made 2 to 3 months prior to sowing (e.g., July to August for maize) but are often revised up to the period preceding planting (Bert et al 2006(Bert et al , 2007.…”

Section: Case Studymentioning

confidence: 99%

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

et al. 2009

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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), Senior authorship is shared. 146Climatic Change (2009) 97:145-170 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.

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Climatic information and decision-making in maize crop production systems of the Argentinean Pampas

Cited by 53 publications

References 51 publications

The land management tool: Developing a climate service in Southwest UK

The land management tool: Developing a climate service in Southwest UK

Use of climate information in soybean farming on the Argentinean pampas

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

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