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Annual forecasts for the Australian macadamia crop have been issued since 2001, with varying (and not always improving) degrees of accuracy. Regression models using climate variables have formed the basis for these forecasts, with general linear model (GLM) ensembles being adopted more recently. This has proven to be a challenging task, as there are only a small number of observations (18) combined with a large number (90+) of independent variables – these being different climate measures for different times of the year (representing 'key physiological periods for macadamia trees'). Also, these 'assumedly-independent' variables contain various degrees of correlation. This study uses cross-validation, with the most recent data for the two dominant production regions of Australia (Lismore and Bundaberg), to investigate the relative performance of alternate modelling methods. These modelling methods were GLMs, partial least squares (PLS) regression and LASSO (least absolute shrinkage and selection operator) penalised regression. Model ensembles, which have been shown to be beneficial in many alternate disciplines, are used to advantage. Both GLMs and PLS produced quite-disappointing results, failing to meet the project's benchmarked accuracy of ±10% error. The optimal LASSO models performed notably better, with a further improvement when ensembles were incorporated. The lowest mean absolute error (MAE) rates here were 9.0% for Lismore and 5.9% for Bundaberg. Hence LASSO ensembles will be adopted for future forecasts of the Australian macadamia crop. • Since 2001 crop forecasts have been provided for the Australian industry to facilitate macadamia operations. • This paper reviews this process, and conducts a cross-validation comparison of some of the more modern regression methods. • These are also incorporated in with the new ensemble modelling methods, also known as 'wisdom of the crowds'. • Results from the ensemble LASSO penalised regressions show a marked improvement over the previous methods. • This method is now being adopted for the 2019 macadamia crop forecast. [ABSTRACT FROM AUTHOR]

Abstract Global and regional scale agricultural monitoring systems aim to provide up-to-date information regarding food production to different actors and decision makers in support of global and national food security. To help reduce price volatility of the kind experienced between 2007 and 2011, a global system of agricultural monitoring systems is needed to ensure the coordinated flow of information in a timely manner for early warning purposes. A number of systems now exist that fill this role. This paper provides an overview of the eight main global and regional scale agricultural monitoring systems currently in operation and compares them based on the input data and models used, the outputs produced and other characteristics such as the role of the analyst, their interaction with other systems and the geographical scale at which they operate. Despite improvements in access to high resolution satellite imagery over the last decade and the use of numerous remote-sensing based products by the different systems, there are still fundamental gaps. Based on a questionnaire, discussions with the system experts and the literature, we present the main gaps in the data and in the methods. Finally, we propose some recommendations for addressing these gaps through ongoing improvements in remote sensing, harnessing new and innovative data streams and the continued sharing of more and more data. Highlights • Eight global and regional agricultural monitoring systems are compared • Gaps in data are described, where cropland maps, crop calendars and meteorological data are viewed as the most critical • Gaps in methods include the need for better predictions of yield and crop production and how to operationalize research methods • New sources of remote sensing data, greater data sharing and new sources of information, e.g. from mobile phones, will all help to improve agricultural monitoring [ABSTRACT FROM AUTHOR]

Abstract Each month the JRC issues the MARS Bulletin detailing the agro-meteorological and expert analysis underpinning the assessment of European crops' status and yield forecasts. In this context a resume is provided to give an overview on the geographical distribution of eventual crop damages. The MARS Bulletin provides such information in a set of synthetic maps (Areas of Concern), produced in each Bulletin, depicting extreme weather events and their impact on crops that have occurred in Europe during the analysis period. The present article describes the mix of quantitative and qualitative datasets and methodologies that drive the delineation of the Areas of Concern (AOC) maps and evaluates their capability to resemble crop production losses. The quantitative analysis is based on the Mars Crop Yield Forecasting System (MCYFS) indicators coming from meteorological models, crop growth models and remote sensing data. Indicators are considered in absolute and relative terms and in their relation with standard statistical metrics. The outcome of the quantitative analysis is a set of potential Areas of Concern. Experts' judgment is thus necessary to discriminate potential results through a qualitative analysis focused on: past occurred events and climatologic conditions; agro-management practices; regional agricultural systems peculiarity and their historical resilience and resistance to adverse conditions. In this article the experts' judgment of the team of current MARS analysts, as used in the AOC analysis, is translated into a warning index. Such index condenses the specific contribution to the final production of each development stage and the adverse agrometeorological events occurred, as depicted into the AOC maps. The warning index is computed at country scale for the past five agricultural seasons, from season 2011-2012 to season 2015-2016. Two crops are considered, winter wheat and grain maize as proxy for winter and summer crop groups. The warning indexes calculated are then compared to the national production in a qualitative way. To support the analysis few study cases are presented. The findings of this article highlight that the events depicted in the AOC maps are informative about production losses and specific knowledge is needed to full understand the information carried. Highlights • The comparison of the issued warnings through AOC analysis shows some good agreements with production anomalies. • AOC analysis properly assessed most of the winter wheat production anomalies. • AOC analysis could provide warning relevant at regional scale only. • AOC analysis tends to underestimate unfavorable conditions at harvest. • This paper represents the first attempt to formalize the tacit knowledge within the group of MARS analysts. [ABSTRACT FROM AUTHOR]

Abstract This paper presents an evaluation framework followed to identify cost-efficient alternatives to extend the MARS Crop Yield Forecasting System (MCYFS), run by the European Commission Joint Research Centre since 1992, to other main producing areas of the world: Eastern European Neighbourhood, Asia, Australia, South America and North America. These new systems would follow the principles and components of the MCYFS Europe: a meteorological data infrastructure, a remote sensing data infrastructure, a crop modelling platform, statistical tools, a team of analysts and a crop area estimation component. The framework designed evaluates the performance of the possible MCYFS-like system realizations against six defined objectives and their costs. Possible monitoring systems are based on a combination of different technical solutions for each of the MCYFS components, and are evaluated through an automatic algorithm that calculates the expected system performance –relying on a priori expert judgement–, the costs, and possible risks to construct some technical solutions, to finally identify the cost-efficient ones. A baseline system, achieving the minimum required performance, was identified as the most efficient starting point for the MCYFS extension in all the geographical areas. Such system would be built upon: (i) near real-time reanalysis meteorological products; (ii) remote sensing data from low-resolution (~1 km) platforms with a long-term product archive; (iii) crop models based on crop-specific model calibration from experimental data published in scientific literature; (iv) statistical methods based on trend and regression analysis applied to national level; (v) a team of analysts with specific technical profiles (on meteorology, remote sensing, and agronomy); and (vi) digital classification of very high resolution imagery supported by non-expensive ground surveys for area estimation. In countries where accessibility to local data and resources is high the baseline system can be upgraded enhancing some of the components: sub-national statistical analysis with additional statistical methods like multiple regression or scenario analysis; recruitment of experts on local agricultural conditions in the team of analysts; local calibration of crop models with experimental data; and exploiting high and low resolution biophysical products from remote sensing for crop monitoring. Highlights • We present a framework to identify cost-efficient solutions for a crop yield forecasting system in the main producing areas. • An automatic algorithm evaluates the expected performance, costs, and possible risks to construct some technical solutions. • The analysis identified a baseline system common to all the geographical areas reaching the minimum required performance. • A roadmap to upgrade the baseline system indicates those solutions improving the system performance at lower costs. [ABSTRACT FROM AUTHOR]

Abstract Since 1993, the JRC has put in operation a crop monitoring and yield forecasting system for Europe, the results of which are published in the JRC MARS Bulletin (currently every month). This paper outlines how the agro-meteorological analyses, country-specific overviews of crop conditions, and crop yield forecasts reported in the Bulletin are used and how these respond to the diverse needs of different types of stakeholders. Stakeholders from more than 32 countries download the JRC MARS Bulletin, in peak-season up to 1500 downloads occur in the first days after publication. The readership of the Bulletin is diverse, coming from governments (e.g. Ministries of Agriculture), private companies (e.g. commodity traders, banking), media, and research and academia. On the list of stakeholders that want to be notified of the release of the Bulletin, roughly 37% originate from business, 35% from research and development, 22% from government, and 6% from the media. The primary user is the Directorate General for Agriculture and Rural Development (DG-AGRI) of the European Commission , which uses the forecasts to quantify the production estimates for crop supply balance sheets and to identify regions with exceptional (mostly weather related) challenges that might require a policy response. The information for wheat, maize and rice is shared through the Agricultural Market Information System (AMIS) thus contributing to increased global market transparency and better governance of agriculture and food policies. The largest business use is in market information, financial, and consultancy services, followed by commodity trading. Examples of use in media reports as well as online feedback to those, e.g. by farmer's organizations, are also presented. Downloads of the Bulletin peak in the month before harvest at the time when the forecasts can be of most value for stakeholder decision-making. Highlights • The primary user of crop yield forecasts & the JRC MARS Bulletin is DG-AGRI. • Stakeholders from more than 32 countries download the JRC MARS Bulletin. • Stakeholders are from business (37%), R&D (35%), government (22%), and media (6%). • Bulletin & yield forecast downloads (up to ~1500) peak in the month before harvest. • Knowledge gaps on actual use, perceived usefulness, impacts of crop forecasts exist. [ABSTRACT FROM AUTHOR]

In this paper, we evaluated the performance of the model AquaCrop for crop yield forecasting in the Brazilian semiarid (BSA) using meteorological observation and Eta model seasonal climate forecasts as input data. The study area is characterized by low rainfall that is poorly distributed throughout the rainy season; thus, the region's agricultural productivity is vulnerable to climate conditions. AquaCrop was first calibrated using field experiments and subsequently applied to simulate an operational crop yield forecast system for maize under rainfed conditions. Simulations were performed with daily data for 37 growing seasons for the period 2001–2010. The seasonal climate forecast was used in combination with observed meteorological data to anticipate the crop forecast. Soil characteristics were derived from pedotransfer functions (PTFs). We were able to demonstrate the ability of the seasonal crop yield forecast system to provide timely and accurate information about maize yield at least 30 days in advance of the harvest. The development of improved crop yield forecasting system is crucial for implementing drought-preparedness measures in the BSA region. [ABSTRACT FROM AUTHOR]

Advances in our ability to predict climate fluctuations months in advance suggest opportunity to improve management of climatic risk in agriculture, but only if particular conditions are in place. This paper outlines prerequisites to beneficial forecast use; highlights key issues, approaches and challenges related to each; and suggests an evolutionary strategy. The first prerequisite is that forecast information must address a need that is both real and perceived. Second, benefit arises only through viable decision options that are sensitive to forecast information. Third, benefit depends on prediction of the components of climate variability that are relevant to viable decisions. Fourth, appropriate forecast use requires effective communication of relevant information. Finally, sustained use requires institutional commitment and favorable policies. It is useful to consider three phases of effort: an exploratory phase to gain understanding and assess potential, a pilot phase characterized by co-learning between researchers and target decision makers, and an operational phase focusing on engaging and equipping relevant institutions. Although examples of use and potential use, and advances in institutional support, are cause for optimism, use of climate prediction by agriculture s still too new to support strong generalizations about its value. [Copyright &y& Elsevier]

Coffee is one of the trademarks of Colombia. Currently, up to a half million Colombian families depend directly on coffee production for their livelihoods. As such, there has been increasing concerns about how coffee productivity will react to changing climate conditions and how coffee growers could adapt their production practices. This paper is one of the first to estimate the production function of Colombian coffee at the municipal level and to make projections about its future productivity. Using a panel dataset measured across municipalities over 2007–2013, we find that productivity depends on altitude as well as on March temperature and precipitation. We estimate projections based on the 2.6, 4.5, and 6.0 Representative Concentration Pathways derived from Global Circulation Models. We find that productivity over 2041–2060 is expected to increase by 7.6% on average. However, we find that this forecast varies greatly according to altitude. Indeed, municipalities above median elevation will increase their productivity by 16%, while those below the median will experience a 8.1% decrease in productivity. Our result implies that place-tailored strategies for coffee production in Colombia are required to adapt to changing climate conditions in the future. [Display omitted] • Colombia is a major coffee producer and there is uncertainty about the effect of climate variability at the municipal level. • We estimate a dynamic panel model and use it to forecast productivity at the municipal level for 2041–2060. • Productivity will increase by 7.6% nation-wide and 16.7% in high-altitude municipalities. It will decrease by 8.16% in low-altitude municipalities. • Although productivity on average will rise, attention must be paid to areas that will be less productive. • The government and coffee institutions must facilitate crop shifting efforts in areas expected to decrease productivity. [ABSTRACT FROM AUTHOR]