Simple econometric models for short term production choices in cropping systems

The aim of this article is to present new models of acreage choices to describe short term production choices. Its construction combines concepts developed in the Positive Mathematical Programming and Multicrop Econometric literatures. They consider land as an allocable fixed input and motivate crop diversification by decreasing returns to crop area and/or implicit costs generated by constraints on acreage choices and by limiting quantities of quasi-fixed factors. Attractive re-parametrization of the standard quadratic production function and different functional forms for cost function are proposed to have parameters easily interpretable and to define econometric models in a very simple way.Acreage share; Production function; Multicrop econometric model; Positive Mathematical Programming

Short-term Marginal Costs in French Agriculture

Farm Management,

Endogeneity of acreage choices in input allocation equations: implied problems and a solution

Replaced with revised version of paper 07/10/09.input allocation, multi-output econometric model, control function approach, Crop Production/Industries, Production Economics,

Variable Input Allocation: Why Heterogeneity Matters?

The allocation of variable inputs among crops is a common problem in applied studies that use farm accountancy data. Standard farm accounting information is typically restricted to aggregate or whole-farm input expenditures; there are usually no details on how these expenditures are split among crops. Most studies employing multi-crop econometric models with land as an allocable fixed input consider generally variable input uses at the farm level (Moore and Negri, 1992). However, the allocation of variable inputs among crops appears to be useful for several objectives, such as to analyze the evolution of gross margins at the crop level, to investigate the empirical validity of a multi-crop econometric model and to provide important information for extension agents or farmer advisors.Variable Input Allocation, heterogeneity, Agricultural and Food Policy, Agricultural Finance, Crop Production/Industries, Farm Management, Research Methods/ Statistical Methods,

Synchronized sweep algorithms for scalable scheduling constraints

This report introduces a family of synchronized sweep based filtering algorithms for handling scheduling problems involving resource and precedence constraints. The key idea is to filter all constraints of a scheduling problem in a synchronized way in order to scale better. In addition to normal filtering mode, the algorithms can run in greedy mode, in which case they perform a greedy assignment of start and end times. The filtering mode achieves a significant speed-up over the decomposition into independent cumulative and precedence constraints, while the greedy mode can handle up to 1 million tasks with 64 resources constraints and 2 million precedences. These algorithms were implemented in both CHOCO and SICStus

Study of cascade ring-closing metathesis reactions en route to an advanced intermediate of Taxol

A highly functionalized intermediate in the synthesis of Taxol has been synthesized, which features the tricyclic core and the required oxygen substituents at C1, C2, C7, C10 and C13. The key step, a ring-closing dienyne metathesis (RCDEYM) reaction, has been thoroughly optimized to favor the tricyclic product over the undesired bicyclic product resulting from diene metathesis

Accounting for agronomic rotations in crop production: A theoretical investigation and an empirical modeling framework

As far as crop acreage choices are concerned, a consensus seems to exist among agricultural scientists and extension agents: crop rotation effects and the related constraints are major determinants of farmers’ crop choices. Crop rotation effects are inherently dynamic. They are generally ignored in multicrop models with land as an allocable input found in the literature since most of these models are developed within a static framework. The aim of this paper is twofold (i) to propose a new approach and tools for investigating dynamic crop acreage choices accounting for crop rotation benefits and constraints and (ii) to illustrate the impacts of crop rotation effects and constraints on farmers’ acreage choices through simulation examples. The models proposed in this paper are sufficiently simple for being empirically tractable either in simulation studies or in econometric and mathematical programming analyses. Our simulation results tend to show responses of the optimal dynamic acreages to simple price shocks which are much more complex than those implied by static models. They also demonstrate that farmers’ perceptions of the future economic context are crucial determinants of their acreage choices. In fact current acreage choices may appear suboptimal in a static sense but are fully consistent when dynamic effects of crop rotations are specified.Crop rotation, Dynamic programming, Acreage choice, Crop Production/Industries, Land Economics/Use, Q12, D21, D24, D92,

The Birth of a Nation (Nat Parker, 2016): The Tale of Nat Turner's Rebellion

International audienc

Quantitative Genetics and Functional-Structural Plant Growth Models: Simulation of Quantitative Trait Loci Detection for Model Parameters and Application to Potential Yield Optimization

Background and Aims: Prediction of phenotypic traits from new genotypes under untested environmental conditions is crucial to build simulations of breeding strategies to improve target traits. Although the plant response to environmental stresses is characterized by both architectural and functional plasticity, recent attempts to integrate biological knowledge into genetics models have mainly concerned specific physiological processes or crop models without architecture, and thus may prove limited when studying genotype x environment interactions. Consequently, this paper presents a simulation study introducing genetics into a functional-structural growth model, which gives access to more fundamental traits for quantitative trait loci (QTL) detection and thus to promising tools for yield optimization. Methods: The GreenLab model was selected as a reasonable choice to link growth model parameters to QTL. Virtual genes and virtual chromosomes were defined to build a simple genetic model that drove the settings of the species-specific parameters of the model. The QTL Cartographer software was used to study QTL detection of simulated plant traits. A genetic algorithm was implemented to define the ideotype for yield maximization based on the model parameters and the associated allelic combination. Key Results and Conclusions: By keeping the environmental factors constant and using a virtual population with a large number of individuals generated by a Mendelian genetic model, results for an ideal case could be simulated. Virtual QTL detection was compared in the case of phenotypic traits - such as cob weight - and when traits were model parameters, and was found to be more accurate in the latter case. The practical interest of this approach is illustrated by calculating the parameters (and the corresponding genotype) associated with yield optimization of a GreenLab maize model. The paper discusses the potentials of GreenLab to represent environment x genotype interactions, in particular through its main state variable, the ratio of biomass supply over demand