Agroecological aspects of evaluating agricultural research and development:

In this paper we describe how biophysical data can be used, in conjunction with agroecological concepts and multimarket economic models, to systematically evaluate the effects of agricultural R&D in ways that inform research priority setting and resource allocation decisions. Agroecological zones can be devised to help estimate the varying, site-specific responses to new agricultural technologies and to evaluate the potential for research to spill over from one agroecological zone to another. The application of agroecological zonation procedures in an international agricultural research context is given special attention.Agricultural research., Technological innovations., Agricultural economics and policies.,

Time tracking of different cropping patterns using Landsat images under different agricultural systems during 1990-2050 in Cold China

Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China contains different agricultural systems (state and private farming), and such systems could lead to different cropping patterns. So far, such changes have not been revealed yet. Based on the Landsat images, this study tracked cropping information in five-year increments (1990-1995, 1995-2000, 2000-2005, 2005-2010, and 2010-2015) and predicted future patterns for the period of 2020-2050 under different agricultural systems using developed method for determining cropland patterns. The following results were obtained: The available time series of Landsat images in Cold China met the requirements for long-term cropping pattern studies, and the developed method exhibited high accuracy (over 91%) and obtained precise spatial information. A new satellite evidence was observed that cropping patterns significantly differed between the two farm types, with paddy field in state farming expanding at a faster rate (from 2.66 to 68.56%) than those in private farming (from 10.12 to 34.98%). More than 70% of paddy expansion was attributed to the transformation of upland crop in each period at the pixel level, which led to a greater loss of upland crop in state farming than private farming (9505.66 km(2) vs. 2840.29 km(2)) during 1990-2015. Rapid cropland reclamation is projected to stagnate in 2020, while paddy expansion will continue until 2040 primarily in private farming in Cold China. This study provides new evidence for different land use change pattern mechanisms between different agricultural systems, and the results have significant implications for understanding and guiding agricultural system development

Modelling Agricultural Diffuse Pollution: CAP – WFD Interactions and Cost Effectiveness of Measures

Within the context of the Water Framework Directive (WFD) and the Common Agricultural Policy (CAP), the design of effective and sustainable agricultural and water resources management policies presents multiple challenges. This paper presents a methodological framework that will be used to identify synergies and trade-offs between the CAP and the WFD in relation to their economic and water resources environmental effects, and to assess the cost-effectiveness of measures to control water pollution, in a representative case study catchment in Scotland. The approach is based on the combination of a biophysical simulation model (CropSyst) with a mathematical programming model (FSSIM-MP), so as to provide a better understanding and representation of the economic and agronomic/environmental processes that take place within the agricultural system.Bio-economic Modelling, Water Framework Directive, Common Agricultural Policy, Agricultural and Food Policy, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy,

Development of a conceptual framework for integrated analysis and assessment of agricultural systems in SEAMLESS-IF

Production Economics,

The Contribution of Genetic Resources and Diversity to Wheat Productivity: A Case from the Punjab of Pakistan

This study makes use of data on wheat production in the Punjab of Pakistan from 1979 to 1985 to 1) examine patterns of varietal diversity in farmers' fields both at the regional and district levels and 2) identify how and in what ways genetic resources have contributed to wheat productivity and yield stability-important considerations to farmers and national authorities where wheat is a staple food crop. Five indicators are used to describe the system of wheat genetic resource use and diversity in farmers' fields. The contribution of farmers' previous selections is expressed as the number of different landraces appearing in the pedigree of a cultivar . The contribution of scientific breeding efforts is expressed as the number of parental combination appearing in a cultivar's pedigree. The diversity of wheat varieties in a geographical area, as related to productivity, is captured by measures of area concentration (diversity in space) and age of varieties (diversity in time). Finally, the relative dissimilarity of cultivars grown in a geographical area is measured using a distance indicator constructed from genealogical information. Disaggregated analysis at the district level demonstrates how diversity patterns are influenced by the production environment and by possible differences in the availability of suitable varieties. The study finds no indication that modern plant breeding technologies have reduced diversity among the wheats grown in the districts of the Punjab of Pakistan during the study period, although brief. Analysis of the genealogical background of the varieties grown by farmers reveals patterns of greater use of genetic resources and dissimilarity of parentage. For some factors related to genetic resource use and diversity, there are large differences between production environments (specifically, irrigated and rainfed areas) and individual districts, which suggest that efforts to increase genetic diversity in farmers' fields will require policy instruments tailored to the individual circumstances of each production environment. Econometric results suggest that greater genealogical dissimilarity and higher rates of varietal replacement are likely to have positive payoffs relative to aggregate yield stability, while in areas where production constraints inhibit farmers' ability to exploit the yield potential of their varieties, better production management is likely to have greater yield enhancing effects than the varietal attributes related to diversity.Crop Production/Industries,

Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.Comment: 19 pages, 10 figure

MODELING OF AGRICULTURAL SYSTEMS

The authors present an overview of agricultural systems models. Beginning with why systems are modeled and for what purposes, the paper examines types of agricultural systems and associated model types. The broad categories range from pictorial (iconic) models to descriptive analogue models to symbolic (usually mathematical) models. The uses of optimization versus non-optimizing mechanistic models are reviewed, as are the scale and aggregation challenges associated with scaling up from the plant cell to the landscape or from a farm enterprise to a world market supply-demand equilibrium Recent modeling developments include the integration of formerly stand-alone biophysical simulation models, increasingly with a unifying spatial database and often for the purpose of supporting management decisions. Current modeling innovations are estimating and incorporating environmental values and other system interactions. At the community and regional scale, sociological and economic models of rural community structure are being developed to evaluate long-term community viability. The information revolution is bringing new challenges in delivering agricultural systems models over the internet, as well as integrating decision support systems with the new precision agriculture technologies.Farm Management,