Special Issue "Practical Use of Crop Models in Agronomy"

A special issue of Agronomy (ISSN 2073-4395).

Deadline for manuscript submissions: closed (30 June 2016).

Special Issue Editor

Dr. Philippe Debaeke
Website
Guest Editor
INRA (Institut National de la Recherche Agronomique), UMR AGIR, CS 52627, 31326 Castanet-Tolosan, France
Interests: interaction genotype by environment; crop modeling; cropping systems; water management; integrated crop protection; sunflower

Special Issue Information

Dear Colleagues,

Numerous crop models have been developed and published since the pioneering approaches of Wageningen and US teams in the 1970s–1980s. Very early, potential uses of these models for agronomical applications were emphasized, and some theoretical and practical examples were published as proofs of concept. Among the examples of model-based applications: field diagnosis, yield prediction, tactical decision making, water allocation among crops, policy scenario analysis, ideotype design, cropping systems design, impact assessment of climate change, etc. A range of dynamic models, from process-based to more simple engineering models, were concerned by these applications. Initially developed by agronomists and crop physiologists, these models became popular in other communities, such as with economists, plant breeders or hydrologists; agronomic models were interfaced or coupled with other disciplinary models to widen the domains of application.

However, crop models have been driven more by the integration of new research than by the practical problems and requirements of end-users. They have not been designed specifically for operational purposes. In order to be fully used by advisers and consultants for instance, they should at least: a) better integrate the effects of crop practices; b) more extensively represent the main biotic and abiotic limiting factors of crop production; c) include parameters and input variables easy to calibrate in actual situations; d) be ergonomic for running multi-simulations and rapidly interpreting the outputs; and e) include a realistic estimate of model uncertainty. As crop modelers have invested more in science than in usability, the adoption of models by decision makers in agriculture has not been as successful as initially expected.

This Special Issue offers the opportunity for crop scientists to publish papers, rapidly, on a topic not so frequently reported in the literature: the “practical use of crop models in agronomy”, with a good visibility. Research articles or reviews reporting novel applications of crop models in agronomy are most welcome. Examples of topics are: crop management and cropping systems design, decision support for tactical and strategical decisions, yield and quality prediction at various time and space scales, water management, variety assessment, etc. In addition, papers analyzing the problems addressed by the practical use of crop models, the participation of end-users in model design and application, the nature of information to integrate in models, or the complementarity of models with other methods used in agronomy (experimentation, survey, etc.) are also welcomed.

Ideally, each paper should clearly identify the intended users, specify if the model is already used, and, if not, how it would be used by the intended users.

Dr. Philippe Debaeke
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Crop model
  • Model application
  • Cropping System Design
  • Crop Management
  • Variety Assessment
  • Yield Prediction
  • End-users

Published Papers (3 papers)

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Research

Open AccessArticle
Climate-Optimized Planting Windows for Cotton in the Lower Mississippi Delta Region
Agronomy 2016, 6(4), 46; https://doi.org/10.3390/agronomy6040046 - 29 Sep 2016
Cited by 14
Abstract
Unique, variable summer climate of the lower Mississippi (MS) Delta region poses a critical challenge to cotton producers in deciding when to plant for optimized production. Traditional 2–4 year agronomic field trials conducted in this area fail to capture the effects of long-term [...] Read more.
Unique, variable summer climate of the lower Mississippi (MS) Delta region poses a critical challenge to cotton producers in deciding when to plant for optimized production. Traditional 2–4 year agronomic field trials conducted in this area fail to capture the effects of long-term climate variabilities in the location for developing reliable planting windows for producers. Our objective was to integrate a four-year planting-date field experiment conducted at Stoneville, MS during 2005–2008 with long-term climate data in an agricultural system model and develop optimum planting windows for cotton under both irrigated and rainfed conditions. Weather data collected at this location from 1960 to 2015 and the CSM-CROPGRO-Cotton v4.6 model within the Root Zone Water Quality Model (RZWQM2) were used. The cotton model was able to simulate both the variable planting date and variable water regimes reasonably well: relative errors of seed cotton yield, aboveground biomass, and leaf area index (LAI) were 14%, 12%, and 21% under rainfed conditions and 8%, 16%, and 15% under irrigated conditions, respectively. Planting windows under both rainfed and irrigated conditions extended from mid-March to mid-June: windows from mid-March to the last week of May under rainfed conditions, and from the last week of April to the end of May under irrigated conditions were better suited for optimum yield returns. Within these windows, rainfed cotton tends to lose yield from later plantings, but irrigated cotton benefits; however, irrigation requirements increase as the planting windows advance in time. Irrigated cotton produced about 1000 kg·ha−1 seed cotton more than rainfed cotton, with irrigation water requirements averaging 15 cm per season. Under rainfed conditions, there is a 5%, 14%, and 27% chance that the seed cotton production is below 1000, 1500, and 2000 kg·ha−1, respectively. Information developed in this paper can help MS farmers in decision support for cotton planting. Full article
(This article belongs to the Special Issue Practical Use of Crop Models in Agronomy)
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Open AccessArticle
Simulating the Probability of Grain Sorghum Maturity before the First Frost in Northeastern Colorado
Agronomy 2016, 6(4), 44; https://doi.org/10.3390/agronomy6040044 - 27 Sep 2016
Cited by 1
Abstract
Expanding grain sorghum [Sorghum bicolor (L.) Moench] production northward from southeastern Colorado is thought to be limited by shorter growing seasons due to lower temperatures and earlier frost dates. This study used a simulation model for predicting crop phenology (PhenologyMMS) to estimate [...] Read more.
Expanding grain sorghum [Sorghum bicolor (L.) Moench] production northward from southeastern Colorado is thought to be limited by shorter growing seasons due to lower temperatures and earlier frost dates. This study used a simulation model for predicting crop phenology (PhenologyMMS) to estimate the probability of reaching physiological maturity before the first fall frost for a variety of agronomic practices in northeastern Colorado. Physiological maturity for seven planting dates (1 May to 12 June), four seedbed moisture conditions affecting seedling emergence (from Optimum to Planted in Dust), and three maturity classes (Early, Medium, and Late) were simulated using historical weather data from nine locations for both irrigated and dryland phenological parameters. The probability of reaching maturity before the first frost was slightly higher under dryland conditions, decreased as latitude, longitude, and elevation increased, planting date was delayed, and for later maturity classes. The results provide producers with estimates of the reliability of growing grain sorghum in northeastern Colorado. Full article
(This article belongs to the Special Issue Practical Use of Crop Models in Agronomy)
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Open AccessArticle
Integrating Wheat Canopy Temperatures in Crop System Models
Agronomy 2016, 6(1), 7; https://doi.org/10.3390/agronomy6010007 - 22 Jan 2016
Cited by 11
Abstract
Crop system models are generally parametrized with daily air temperatures recorded at 1.5 or 2 m height. These data are not able to represent temperatures at the canopy level, which control crop growth, and the impact of heat stress on crop yield, which [...] Read more.
Crop system models are generally parametrized with daily air temperatures recorded at 1.5 or 2 m height. These data are not able to represent temperatures at the canopy level, which control crop growth, and the impact of heat stress on crop yield, which are modified by canopy characteristics and plant physiological processes Since such data are often not available and current simulation approaches are complex and/or based on unrealistic assumptions, new methods for integrating canopy temperatures in the framework of crop system models are needed. Based on a forward stepwise-based model selection procedure and quantile regression analyses, we developed empirical regression models to predict winter wheat canopy temperatures obtained from thermal infrared observations performed during four growing seasons for three irrigation levels. We used daily meteorological variables and the daily output data of a crop system model as covariates. The standard cross validation revealed a root mean square error (RMSE) of ~0.8 °C, 1.5–2 °C and 0.8–1.2 °C for estimating mean, maximum and minimum canopy temperature, respectively. Canopy temperature of both water-deficit and fully irrigated wheat plots significantly differed from air temperature. We suggest using locally calibrated empirical regression models of canopy temperature as a simple approach for including potentially amplifying or mitigating microclimatic effects on plant response to temperature stress in crop system models. Full article
(This article belongs to the Special Issue Practical Use of Crop Models in Agronomy)
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