Crop
Modeling
In silico approaches for informing key farm decisions
Current research projects in this area:
Ph.D. Student – Ignacio Massigoge
Ecophysiology | Crop Modeling
Traditionally, increasing yield has been a successful way to increase global production without land expansion, yet this task is becoming more challenging for both breeders and agronomists. Therefore, rotation intensification, associated with more crops per year, emerges as a valuable strategy for meeting an increased food demand. However, this alternative is scarcely tested in most of the cropland area in North America, as the dominant production systems are cereal or legume on a single crop per year rotation.
Furthermore, the explored temporal intensified cropping systems have been associated with summer legumes and winter cereal rotational schemes.
We hypothesized that unexplored cereal–legume double summer crop system provides untapped potential to increase land productivity of suitable farming systems in the Southern US region.
The primary objective of this research project was to evaluate the feasibility of cultivating maize-soybean double crops in the Southern US through field data and crop simulation using the APSIM model framework.
Read our publication: https://doi.org/10.1016/j.agsy.2022.103539
Project funded by Corteva Agriscience.
Exploring opportunities for crop intensification during the summer season: maize-soybean double crop in the Southern US
The US central Great Plains (Kansas-Oklahoma) region is a major producer of field crops, accounting for roughly 20 MT of maize production. Despite the relevance of the region, most of the literature regarding optimizing management strategies has focused on the northern corn-belt. Therefore, further efforts are needed to provide farmers with this critical knowledge.
The primary objective of this research project is to explore planting date by hybrid maturity combinations via a crop growth model using the Agricultural Production System Simulator (APSIM) with phenology calibrated hybrids to define environment-tailored management strategies.
Read our publication: https://doi.org/10.1016/j.eja.2023.126905
Read our extension article: https://www.bookstore.ksre.ksu.edu/pubs/MF3610.pdf
For more information and the current state of the project, please visit Ignacio’s website at:
Ignacio's Website
Maize planting date and maturity in the US central Great Plains: Exploring windows for maximizing yields
Ph.D. Student – Ignacio Massigoge
Ecophysiology | Crop Modeling
Project funded by the Rainfed Agriculture Innovation Network and Corteva Agriscience.
Post Doctoral Researcher - Ana Carcedo
Ecophysiology | Crop Management
Kansas is the number one sorghum producer in the United States, yet the number of acres sown each year is reducing. Sorghum represents an interesting crop with multiples features such as plasticity, low input demand, and tolerance to different adverse conditions. Furthermore, the relevance of keeping sorghum in the crop rotations lays on maintaining the diversification of the systems and reducing the vulnerabilities related to productions similar to monocultures. Therefore, our team through modeling approaches aim to help farmers to develop management strategies, and breeder in the selection process.
The current projects include: i) Environment characterization in sorghum by modeling water-deficit and heat patterns in the Great Plains region, US; ii) Source-sink relation simulation improvement in APSIM modules iii) Exploration of the canopy architecture to maximize the use of resources.
Project funded by Sorghum checkoff, G2P bridge and, Corteva Agriscience
Grain Sorghum – Multiple projects
M.S Student – Lucia Marziotte
Ecophysiology | Crop Management
Increasing crop yields but at the same time improving the sustainability of farming systems is a current challenge. We explore options to diversify and intensify the crop rotations using early-maturing sorghum hybrids to achieve a better fit in the cropping season (reducing the crop duration and placing the dry-down in warmer favorable conditions). Considering that early-maturing hybrids might have a setback due to the shorter cycle also implies less yield potential (linked to less number of leaves and longer time to maximize radiation capture). Then, modeling approaches can be used to explore the plant architecture, optimizing crop layout and arrangement, to determine how light capture can be maximized and the impacts on crop production. Employing crop models (APSIM) and 3D modeling, the study aims: i) explore the optimal canopy architecture (number of leaves and plant size) that does not reduce significantly yield relative to high-yielding late-maturing hybrids ii) provide a characterization of the potential for early-maturing sorghum hybrids to fit in a rotation.
Project funded by Corteva Agriscience and Sorghum Checkoff
Light canopy distribution
.jpg)
Assessing soybean water stress patterns and ENSO occurrence in Southern Brazil: an in-silico approach
M.S Student – Gabriel Hintz
Crop physiology
Location: Rio Grande do Sul State, Brazil.
Project funded by Cooperativa Central Gaucha (CCGL)
Water stress is one of the most important abiotic stresses worldwide, responsible for crop yield penalties and impacting food supply. The frequency and intensity of weather stresses are relevant to delimitating agricultural regions. In addition, El Nino Southern Oscillation (ENSO) has been employed to forecast the occurrence of seasonal water stress.
Lastly, planting date and cultivar maturity selection are key management strategies for boosting soybean (Glycine max (L.) Merr.) yield and mitigating stress-related yield penalties. This study aimed to (i) characterize the water stress patterns and their frequency within regions in Southern Brazil and their relations with ENSO phases; and (ii) assess planting date and cultivar interaction effect on yield and yield uncertainty across water stress patterns.
Previous research projects in this area:
Post Doctoral Researcher - Nilson Vieira Junior
Crop Physiology
Pearl millet is the most cultivated cereal in Senegal, occupying more than 60% of the arable area, and playing a critical role in food security for the livelihood of smallholder farmers and their communities. Pearl millet can be used as a dual-purpose crop (human food and animal feed). However, current productivity levels are considered low and highlight the need to identify new opportunities for innovation to increase productivity through a sustainable intensification framework. Employing crop modeling, this study aims to i) evaluate the interactions between genotype, environment (weather), and management (GxExM) to optimize pearl millet systems and ii) develop regional-specific recommendations based on multiple GxExM scenarios simulated for pearl millet in Senegal.
Project funded by USAID and SIIL Lab