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Posts Tagged ‘DSSAT’

Virtual potato crop modeling: A comparison of genetic coefficients of the DSSAT-SUBSTOR potato model with breeding goals for developing countries

ReportVirtual crop modeling is the representation of future genetic improvements from plant breeding in crop growth simulation models through changes in genetic coefficients or other crop model parameters with the objective of analyzing ex-ante the impacts of improved traits on crop yields and assisting breeders in their breeding efforts.

As a first step towards virtual crop modeling for the potato crop, a new working paper provides a comparison of priority breeding targets for developing country regions with genetic coefficients and other parameters of the SUBSTOR-potato model, thereby showing the potential uses of the model for that purpose.

It is shown that SUBSTOR provides scope for virtual crop modeling. Out of nine priority target traits, five can currently be dealt with in model. Adaptation to long day conditions and heat tolerance can directly be represented by adjusting the genetic coefficients of the model. High yields and drought tolerance would require changes in parameters that are currently included in the model code. Earliness would require the implementation of a new parameter in the code. Additional traits related to crop quality and resistance to biotic stress factors will require more profound changes in either the model structure or the coupling of the crop growth model with disease models.

The full working paper Virtual potato crop modeling: A comparison of genetic coefficients of the DSSAT-SUBSTOR potato model with breeding goals for developing countries is available on ZENODO. The paper is intended as an entry point for discussions and further about how to best carry out virtual crop modeling for the potato crop.

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Crop model calibration with yield trial data: Dealing with missing soil data

A previous blog post discussed how to deal with missing weather data when calibrating a crop model with incomplete yield trial data. A further common problem associated with the use of field trial data from crop breeders for the purpose of calibration of the DSSAT-SUBSTOR  potato model is missing or insufficient soil data.

The DSSAT soil water and nutrient routines require information of the soil found at the site, consisting of a broad range of soil physical and chemical parameters taken from different depths of the soil. The soil data provided along with yield trial data, however, often only consists of information on pH, nutrient availability, organic matter content and soil texture, taken from a sample at one single depth. In this situation, how can we obtain a complete soil profile to be used in DSSAT? Read more…

Crop model calibration with yield trial data: Dealing with missing weather data

06/09/2013 1 comment

One of our tasks in the Global Futures project was to calibrate potato cultivars in the DSSAT-SUBSTOR potato crop model with field trial data requested from CIP breeders. A common problem with the use of that kind of data was that weather data was missing. In most cases, only maximum and minimum temperature, as well as rainfall measured during the cropping season are available. The crop model, however, in addition requires solar radiation data. Furthermore, in order to carry out simulations with different planting or harvest dates, data which goes beyond the original cropping period is needed.

The approach we took to obtain a complete set of weather data that can be used with the crop model rests upon data provided by the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program. It consists of the following steps:
Read more…

Global Futures for Agriculture

16/10/2012 3 comments

My principal activity at CIP is the work in the Global Futures for Agriculture Project. Global Futures, an IFPRI-led project, is designed to assess alternative options for improving agricultural productivity in developing countries. The project is focused on the evaluation of promising technologies for agricultural production in order to identify investments with the highest potential benefits and thereby support the CGIAR in priority setting and strategic planning.

Further objectives of the project are to deepen our understanding of the complex linkages among socioeconomic and environmental change, the functioning of agricultural systems and human well-being and to provide an improved representation of agricultural systems and their potential role in enhancing human well-being. A comprehensive modeling environment integrating socioeconomic, biophysical, and technological responses to simulate global, regional and local consequences of technology investments in the context of changing policies and natural resource threats is developed and applied.

To achieve the goals of the project, economists, plant breeders and crop modelers cooperate in the project to obtain estimates of likely productivity changes brought about by technological innovations for the mandate crops of the nine CGIAR Centers which participate in the project and the subsequent assessment of economic and food security impacts of these changes.

The project employs and enhances IFPRI’s International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), a state-of-the-art economic model that projects the future production, consumption, and trade of key agricultural commodities, and can assess effects of climate change, water availability and other major trends. This model is coupled with crop models of the DSSAT crop modeling system which provides inputs on productivity impacts of virtual agricultural technologies under different scenarios of climate change.