Mapping climatic risks in the EU agriculture

Replaced with revised version of paper 11/18/08.Agrometeorological models, climatic risk, European Union, Vegetation indices, Environmental Economics and Policy, Risk and Uncertainty,

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model

Broadacre farmers adapting to a changing climate

Abstract Data on the financial performance of a diverse set of 249 farm businesses in south-western Australia over the period 2002 to 2011 was collated and analysed.  These 10 years were a period of challenging weather years, underpinned by a warming and drying trend in the region’s climate, frost events and marked price volatility.Based on a range of metrics, almost two-thirds (64%) of the farms in the sample were classed as growing or strong. A less secure group of farms that are at some potential financial risk formed 15% of the farm sample. Over the study period farm profitability, on average, improved, supported by productivity growth, in spite of no underlying improvement in the farmers’ terms of trade.  Productivity improvement allowed most farm businesses, especially crop and mixed enterprise farm businesses, to prosper.  The pathway to their profitability was not so much by investing in new technologies that may have shifted outwards farms’ production possibilities, but rather through better use of existing technologies, including technologies that offered scale economies. Also farmers’ shift into greater dependence on cropping, especially wheat production, was shown to be a sensible and successful adaptation strategy in many regions of south-western Australia, particularly the northern grainbelt.The unique and particular characteristics of each farm business were the main determinant of their business success.  However, a few generalisations apply.  Due to seasonal and market conditions during the study period more farms in the northern parts of the grainbelt in south-western Australia fared better.  Also farmers whose businesses grew strongly over the study period on average displayed superior management capabilities and choices in many areas of farm management.  In addition, these farmers were often more connected to their local community and achieved greater work-life balance.We conclude that as long as broadacre farmers in south-western Australia have on-going access to improved crop varieties and technologies that support the profitable growing of crops, especially wheat; and that they have access to farm management and business education then farmers are likely to be able to adapt to projected climate change.  Provided that a farmer’s terms of trade does not become unduly adverse, and that farmers sensibly manage farm debt, then it seems highly likely that farmers who continue to rely on crop production, mostly wheat-growing, will persist as financially sound businesses in most parts of the study region, even in the face of projected climate change.Please cite this report as:Kingwell, R, Anderton, L, Islam, N, Xayavong, V, Wardell-Johnson, A, Feldman, D, Speijers, J 2013 Broadacre farmers adapting to a changing climate, National Climate Change Adaptation Research Facility, Gold Coast. pp.171.Data on the financial performance of a diverse set of 249 farm businesses in south-western Australia over the period 2002 to 2011 was collated and analysed.  These 10 years were a period of challenging weather years, underpinned by a warming and drying trend in the region’s climate, frost events and marked price volatility.Based on a range of metrics, almost two-thirds (64%) of the farms in the sample were classed as growing or strong. A less secure group of farms that are at some potential financial risk formed 15% of the farm sample. Over the study period farm profitability, on average, improved, supported by productivity growth, in spite of no underlying improvement in the farmers’ terms of trade.  Productivity improvement allowed most farm businesses, especially crop and mixed enterprise farm businesses, to prosper.  The pathway to their profitability was not so much by investing in new technologies that may have shifted outwards farms’ production possibilities, but rather through better use of existing technologies, including technologies that offered scale economies. Also farmers’ shift into greater dependence on cropping, especially wheat production, was shown to be a sensible and successful adaptation strategy in many regions of south-western Australia, particularly the northern grainbelt.The unique and particular characteristics of each farm business were the main determinant of their business success.  However, a few generalisations apply.  Due to seasonal and market conditions during the study period more farms in the northern parts of the grainbelt in south-western Australia fared better.  Also farmers whose businesses grew strongly over the study period on average displayed superior management capabilities and choices in many areas of farm management.  In addition, these farmers were often more connected to their local community and achieved greater work-life balance.We conclude that as long as broadacre farmers in south-western Australia have on-going access to improved crop varieties and technologies that support the profitable growing of crops, especially wheat; and that they have access to farm management and business education then farmers are likely to be able to adapt to projected climate change.  Provided that a farmer’s terms of trade does not become unduly adverse, and that farmers sensibly manage farm debt, then it seems highly likely that farmers who continue to rely on crop production, mostly wheat-growing, will persist as financially sound businesses in most parts of the study region, even in the face of projected climate change

Assessment of the potential impacts of plant traits across environments by combining global sensitivity analysis and dynamic modeling in wheat

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites x 125 years), management practices (3 sowing dates x 2 N fertilization) and $CO_2$ (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait x environment x management landscape ($\sim$ 82 million individual simulations in total). The patterns of parameter x environment x management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identifcation of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.Comment: 22 pages, 8 figures. This work has been submitted to PLoS On

The effects of climate change and variation in New Zealand: An assessment using the CLIMPACTS system

Along with a need to better understand the climate and biophysical systems of New Zealand, the need to develop an improved capacity for evaluating possible changes in climate and their effects on the New Zealand environment has been recognised. Since the middle of 1993 the CLIMPACTS programme, has been focused on the development of such a capacity, in the first instance for the agricultural sector. the goals of this present assessment are: 1. To present current knowledge on likely scenarios of climate change and associated uncertainties in New Zealand; 2. To present current knowledge, based on quantitative analyses using a consistent set of scenarios, on the likely effects of climate change on a range of agricultural and horticultural crops of economic importance; 3. To demonstrate, by way of this report and the associated technical report, the capacity that has been developed for ongoing assessments of this kind in New Zealand. This report has been prepared for both the science and policy communities in New Zealand. There are two main components: 1. The detailed findings of the assessment, presented in a series of chapters; 2. An annex, which contains technical details on models used in the assessment

Use of soil moisture information in yield models

There are no author-identified significant results in this report

Trees and water: smallholder agroforestry on irrigated lands in Northern India

Trees / Populus deltoids / Agroforestry / Afforestation / Reforestation / Models / Water use / Water balance / Evapotranspiration / Precipitation / Remote sensing / Irrigation requirements / India

Mapping Crop Cycles in China Using MODIS-EVI Time Series

As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data

Global and local economic impacts of climate change in Syria and options for adaptation:

There is broad consensus among scientists that climate change is altering weather patterns around the world. However, economists are only beginning to develop tools that allow for the quantification of such weather changes on countries' economies and people. This paper presents a modeling suite that links the downscaling of global climate models, crop modeling, global economic modeling, and subnational-level computable equilibrium modeling. Important to note is that this approach allows for decomposing the potential global and local economic effects on countries, including various economic sectors and different household groups. We apply this modeling suite to Syria, a relevant case study given the country's location in a region that is consistently projected to be among those hit hardest by climate change. Despite a certain degree of endogenous adaptation, local impacts of climate change (through declining yields) are likely to affect Syria beyond the agricultural sector and farmers and also reduce economy-wide growth and incomes of urban households in the long term. The overall effects of global climate change (through higher food prices) are also negative, but some farmers can reap the benefit of higher prices. Combining local and global climate change scenarios shows welfare losses across all rural and urban household groups of between 1.6 – 2.8 percent annually, whereas the poorest household groups are the hardest hit. Finally, while there is some evidence that droughts may become more frequent in the future, it is clear that even without an increase in frequency, drought impacts will continue to put a significant burden on Syria's economy and people. Action to mitigate the negative effects of climate change and variability should to be taken on the global and local level. A global action plan for improving food security and better integration of climate change in national development strategies, agricultural and rural policies, and disaster risk management and social protection policies will be keys for improving the resilience of countries and people to climate change.Climate change, Development, drought, Growth, Poverty,

APPLICATIONS OF UAS IMAGERY IN WHEAT BREEDING

Plant breeding is a field of study with goals that have not changed significantly over time: develop cultivars with high yield, disease resistance, and drought tolerance, to name a few. While the goals of a breeding program may not change frequently, the form and technology used with which those goals are achieved are constantly evolving. High throughput phenotyping (HTP) with unoccupied aerial systems (UAS) shows significant promise in improving how crops are bred. Data collected from UAS can provide a breeder with new insights into how cultivars respond to stress and a particular environment, creating potential use cases for improving other areas of breeding, such as genomic selection and how field experiments are designed and analyzed. These new technologies, however, should not be adopted without consideration. The first study, outlined here, utilized three different HTP platforms and collection methodologies, two ground systems and one UAS-based, to determine if there is a difference in the quality of data collected. Across four years, data collected from ground systems only moderately correlated to UAS. It was also shown that data collected with UAS produced more heritable data than that collected with either ground-based system. While manufacturing specifications of the data collected from remote sensors may be similar, it is essential to be aware of the methodology used in the collection. Reflectance data standardization, sensor platform, and environmental conditions can significantly impact the quality of the data obtained and limit utility across platforms and methodologies. In the second study, spectral reflectance indices (SRI) were evaluated for their ability to improve genomic selection (GS). SRIs collected on 11,593 plots across four years were used with genomic data in univariate models as covariates and in multivariate models as secondary response variables for the assessment of prediction accuracy of grain yield. Including SRI data as covariates in univariate genomic prediction models improved prediction accuracy over the control GS model but was unreliable across years. In multivariate models, SRIs improved prediction performance across years, but due to the dataset size, high-performance computational resources were required, which could limit feasibility in an applied setting. The final study highlights the potential for SRI to improve how a breeder deals with field variability in yield trial experiments. Across three years, 47 breeding trials were evaluated under three spatial analysis strategies: linear models incorporating block-effect, row-column effect, and 2D splines. Model fit was improved across all spatial analysis methods when SRIs were incorporated as covariates. Model fitness was most greatly improved in unreplicated early-generation trials. This study highlighted the potential of SRIs to enhance how breeding trials are analyzed despite extreme environmental variables and climate conditions. This collective research highlights the challenges and benefits of utilizing UAS imagery in an applied breeding pipeline. When used strategically, the insights gained from UAS will, like genomic selection, make it an invaluable tool in the plant breeder's toolbelt