Improvement of the soil-crop model AZODYN under conventional, low-input and organic conditions

The use of mechanistic crop modelling, simulating the dynamics of crop N requirements and nitrogen supply from the soil and fertilizers, can provide sound advice to users. This paper describes a methodological way to improve soil-crop modeling used for N management of conventional and organic wheat

A hedonic approach to estimating the supply of variety attributes of a subsistence crop:

"The paper extends the household hedonic model, as a non-market valuation tool, by estimating a supply function for variety attributes of a subsistence crop in a developing country. The model is applied to bananas in Uganda, making use of disaggregated data on variety-specific farm-gate banana bunch prices and attributes. The hedonic analysis is applied at the farm-gate, the first link in the market chain, while accounting for the semi-subsistence nature of banana producing households. Within the framework of the agricultural household, where consumption and production decisions are non-separable, prices reflect the implicit marginal valuation of both consumption and production attributes jointly. The paper is motivated by the need to quantify the value of banana attributes in light of targeted efforts for variety improvement. Whether variety improvement will pay-off at the market level requires a more detailed examination of the relative worth of banana attributes within the structure of consumer preferences and production technologies related to bananas in Uganda. By revealing important price-attribute relationships, the findings provide guidance for future crop improvement efforts and diversification choices, while taking into account implicit market signals for output characteristics." Author's Abstractsmall farms, Households Models, agricultural sector, Crops Economic aspects, Crop diversification, Variety attributes, Decision-making,

Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm

Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties. The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available yield data. Maps of AWC with a resolution of 10 m were produced across a dryland grain farm in Australia. For certain years and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense dataset

Modelling predicts that heat stress and not drought will limit wheat yield in Europe

Global warming is characterised by shifts in weather patterns and increases in extreme weather events. New crop cultivars with specific physiological traits will therefore be required if climate change is not to result in losses of yield and food shortages. However, the intrinsic uncertainty of climate change predictions poses a challenge to plant breeders and crop scientists who have limited time and resources and must select the most appropriate traits for improvement. Modelling is, therefore, a powerful tool to identify future threats to crop production and hence targets for improvement. Wheat is the most important crop in temperate zones, including Europe, and is the staple food crop for many millions of humans and their livestock. However, its production is highly sensitive to environmental conditions, with increased temperature and incidence of drought associated with global warming posing potential threats to yield in Europe. We have therefore predicted the future impacts of these environmental changes on wheat yields using a wheat simulation model combined with climate scenarios based on fifteen global climate models from the IPCC AR4 multi-model ensemble. Despite the lower summer precipitation predicted for Europe, the impact of drought on wheat yields is likely to be smaller than at present, because the warmer conditions will result in earlier maturation before drought becomes severe later in the summer. By contrast, the probability of heat stress around flowering is predicted to increase significantly which is likely to result in considerable yield losses for heat sensitive wheat cultivars commonly grown in north Europe. Breeding strategies should therefore focus on the development of wheat varieties which are tolerant to high temperature around flowering, rather than on developing varieties resistant to drought which may be required for other parts of the world

Model Plants and Crop Improvement

Within the past decade, there has been an explosion of research in both the public and private sectors regarding the use of plant genetic models to improve crop yield. Bringing together experts from across the globe, Model Plants and Crop Improvement provides a critical assessment of the potential of model plant species for crop improvement. The first comprehensive summary of the use of model plant systems, the book delineates the model species' contribution to understanding the genomes of crop species. The book provides an in-depth examination of the achievements and limitations of the model paradigm. It explores how continued research in models can contribute to the goal of delivering the outputs of molecular biology to crops. Covering the major genetic models such as Arabidopsis thaliana, Lotus japonicus, and Medigago, the book goes on to discuss applications to food plants of global importance including rice, canola, and legumes. The book introduces the evolutionary, genetic, genomic, and morphological attributes of B. distachyon that make it such an attractive new model plant system. As the post-genomic era dawns, a key question to address is how this growing body of genetic and biological information can be extended beyond the model to the modeled species. This book takes you one step closer to applying modeling results to crops in the field

On farm conservation of rice biodiversity in Nepal: a simultaneous estimation approach

"This paper presents an empirical case study about farmer management of rice genetic resources in two communities of Nepal, drawing on interdisciplinary, participatory research that involved farmers, rice geneticists, and social scientists. The decision-making process of farm households is modeled and estimated in order to provide information for the design of community-based conservation programs. A bivariate model with sample selection treats the simultaneous process of whether farmers decide to plant landraces or modern varieties, and whether the landraces they choose to plant constitute genetic diversity of interest for future crop improvement. Findings show that the two landrace choices are affected by different social and economic factors. The estimation procedure demonstrates that in certain cases, however, the decision processes are interrelated. Policies to promote the conservation of local rice diversity will need to take both processes into account. Fitted equations are then used to compare the likelihood that households targeted for conservation according to one set of conservation criteria also meet other conservation criteria. Households most likely to plant landraces identified as important for crop improvement also grow richer, more spatially diverse rice varieties. In these communities, few policy trade-offs would result from employing one set of criteria instead of the other." Authors' AbstractLandraces, Crop diversity,

Representative Farms Economic Outlook for the January 2001 FAPRI/AFPC Baseline

The farm level economic impacts of projected long term prices under the Federal Agriculture Improvement and Reform Act of 1996 (FAIR) on representative crop and livestock operations are projected in this report. For this report the FAIR Act will be referred to as the 1996 Farm Bill. The analysis was conducted over the 1996-2005 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate farming operations in the nation’s major production regions came from two sources: - Producer panel cooperation to develop economic information to describe and simulate representative crop, livestock, and dairy farms. - Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) January 2001 Baseline. The primary objective of the analysis is to determine the farms’ economic viability by region and commodity throughout the life of the 1996 Farm Bill and beyond.Agribusiness, Agricultural and Food Policy, Crop Production/Industries,

Can Dispersed Biomass Processing Protect the Environment and Cover the Bottom Line for Biofuel?

This paper compares environmental and profitability outcomes for a centralized biorefinery for cellulosic ethanol that does all processing versus a biorefinery linked to a decentralized array of local depots that pretreat biomass into concentrated briquettes. The analysis uses a spatial bioeconomic model that maximizes predicted profit from crop and energy products, subject to the requirement that the biorefinery must be operated at full capacity. The model draws upon biophysical crop input-output coefficients simulated with the EPIC model, as well as input and output prices, spatial transportation costs, ethanol yields from biomass, and biorefinery capital and operational costs. The model was applied to 82 cropping systems simulated across 37 sub-watersheds in a 9-county region of southern Michigan in response to ethanol prices simulated to rise from $1.78 to$3.36 per gallon. Results show that the decentralized local biomass processing depots lead to lower profitability but better environmental performance, due to more reliance on perennial grasses than the centralized biorefinery. Simulated technological improvement that reduces the processing cost and increases the ethanol yield of switchgrass by 17% could cause a shift to more processing of switchgrass, with increased profitability and environmental benefits.Biomass production, bioenergy supply, cellulosic ethanol, environmental trade-off analysis, bioeconomic modeling, EPIC, spatial configuration, local biomass processing, Crop Production/Industries, Environmental Economics and Policy, Production Economics, Resource /Energy Economics and Policy, Q16, Q15, Q57, Q18,

Uncovering Productivity Growth in the Disaggregate: Indonesia's Dueling Agricultural Sub-Sectors

The success of seed-fertilizer technologies and government subsidies in attaining nearly self-sufficient rice production in the mid-1980s encouraged the Indonesian government soon afterward to shift resources away from food crops and toward export-oriented crops. These shifts were reinforced by trade liberalization and a sharp devaluation of the rupiah after the 1997 Asian financial crisis, which exerted Indonesia’s comparative advantage in tropical perennial products. In the present paper, we ask whether such events have altered Indonesia’s agricultural growth strategy from a food-crop to an export-crop one. With an innovative multi-output stochastic distance frontier model and provincial production and policy-related data from 1985 to 2005, we estimate technology growth by agricultural subsector and efficiency improvement by political jurisdiction. The perennial-crop sector is found to have achieved the highest technology growth rate, followed by the livestock and annual-crop sectors. We find overall productivity growth to have been moderate, and suggest that little of it can be attributed to Indonesia’s public research efforts.agricultural research, Indonesia, Shephard distance function, stochastic frontier, technical change, technical efficiency, International Development, Productivity Analysis, Research and Development/Tech Change/Emerging Technologies,

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives. © 2019, The Author(s)