🌾 Enhancing sorghum yield and risk management via optimizing #crop design
by Genevieve Durrington, Joseph Saddigh, Jason Brider, Graeme Hammer, Alex Wu
buff.ly/Ik0iT34 #PlantScience #APSIM

Characterizing wheat and barley growth and phenology using multi-spectral remote sensing for site-specific precision agriculture Abstract. Crop phenology informs in-season management practices such as fertilizer and pest and disease controls to optimize final yield. However, tracking

🌾 Characterizing #wheat and barley growth and phenology using multi-spectral remote sensing for site-specific precision agriculture
by Yan Zhao, Ruizhu Jiang, Jason Brider, Scott Chapman, Andries Potgieter
buff.ly/fcFKaBy #PlantScience #APSIM

🌾🌧️ A comparison of empirical and mechanistic #models for #wheat yield prediction at field level in Moroccan rainfed areas
by Achraf Mamassi, Marie Lang, Bernard Tychon, Mouanis Lahlou, Joost Wellens, Mohamed El Gharous, Helene Marrou
doi.org/10.1093/insi... #APSIM #PlantScience

Cropbox: a declarative crop modelling framework Abstract. We introduce Cropbox, a novel modelling framework that supports various aspects of crop modelling in a unique yet concise style. Building a crop

💻🌾 Cropbox: a declarative #crop #modelling framework
doi.org/10.1093/insi... #APSIM #PlantScience

2 open scientist positions with Mitti labs in Bengaluru, India :
Crop Modeler - Climate & GHG Emissions
Machine Learning Engineer - Geospatial AI
apply.workable.com/mitti-labs/ #PlantSciJobs #DSSAT #APSIM

A new study using high-resolution S2 imagery to track key phenological stages of #wheat and #barley
doi.org/10.1093/insi... #PlantScience #APSIM #RemoteSensing #phenology

🌾 🧬 Genotype-specific P- spline response surfaces assist interpretation of regional #wheat adaptation to climate change
doi.org/10.1093/insi... #model #PlantScience #APSIM

Latest paper: Time Series Dataset of Phenology, Biomass, and Chemical Composition of Cassava (Manihot esculenta Crantz) as Affected by Time of Planting and Variety Interactions in Field Trials at Koronivia, Fiji www.mdpi.com/3416082 #mdpidata via @Data_MDPI #cassava #cyanide #APSIM #foodsecurity

Man sieht den Wachstumsverlauf von Silo Mais für 2 Standorte in der gui des Modells APSIM

Man sieht die Pflanzenparametrisierung für einen Beispielmais in der gui von APSIM

Wie kann eigentlich biophysikalische #Modellierung im #Arbeitsalltag aussehen? Doktorandin Irina arbeitet mit #APSIM, was als eins der wenigen Pflanzen-Modelle auch eine richtige #gui hat. Trotzdem gibts noch viele Parameter und Einstellungen, die über C# und R angesprochen werden müssen.

NEW!
🌾 Enhancing #sorghum #yield and risk management via optimizing crop design
by Genevieve Durrington, Joseph Saddigh, Jason Brider, Graeme Hammer, and Alex Wu
doi.org/pwnr #APSIM #model #PlantScience @coeplantsuccess.bsky.social

NEW!
Enhancing #Sorghum Yield and Risk Management via Optimising Crop Design
by Genevieve Durrington, Joseph Saddigh, Jason Brider, Graeme Hammer, and Alex Wu

doi.org/10.1093/insi... #APSIM #model #PlantScience

Streams and Sessions – MODSIM2025

Calling #APSIM modellers!
MODSIM2025 is held 30 Nov-4 Dec 2025. The submission portal is now open for 1-2p abstracts or 7p full papers. Lots of interesting sessions, including an #APSIM symposium: see www.mssanz.org.au/modsim2025/s...
Come join us with your latest APSIM developments or applications!

🍃 Predicting #barley performance in Denmark using #APSIM
by Mercy Appiah, Gennady Bracho-Mujica, Simon Svane, Merete Styczen, Kurt-Christian Kersebaum, Reimund P Rötter
doi.org/pfr6 #model #PlantScience

🍃 Predicting #barley performance in Denmark using #APSIM
by Mercy Appiah, Gennady Bracho-Mujica, Simon Svane, Merete Styczen, Kurt-Christian Kersebaum, Reimund P Rötter doi.org/pfr6 #model
#PlantScience

Leveraging High-Quality Data for Improved Crop Simulations Researchers improve model prediction accuracy by closing existing data gaps.

Insights from utilizing data of different quality levels for simulating #barley performance under Nordic conditions: #APSIM #model evaluation
📰 Story: https://buff.ly/3BK9vyT via @Botany.One
…

💻 🌿 A comprehensive evaluation of the factors affecting the relationship between #soybean canopy traits and seed yield
by Mariana V Chiozza, Kyle Parmley, William T Schapaugh, Antonio R Asebedo, Asheesh K Singh, Fernando E Miguez
https://buff.ly/3Y9xFfn #Model #PlantScience #APSIM

💻 🍃 Predicting #barley performance in Denmark using #APSIM
by Mercy Appiah, Gennady Bracho-Mujica, Simon Svane, Merete Styczen, Kurt-Christian Kersebaum, Reimund P Rötter
https://buff.ly/3W6FZet #model #PlantScience 🧪

Evaluation of the APSIM common bean model using different cultivars and water-management scenarios <p>The common bean (<italic>Phaseolus</italic> <italic>vulgaris</italic>) is a widely consumed legume worldwide and holds significant value in terms of direct human consumption, surpassing all other…

Evaluation of the #APSIM common #bean #model using different cultivars and water-management scenarios
by Seyedreza Amiri, Nader Zakeri and Tayebeh Yousefi

https://buff.ly/49ibO95 via Technology in Agronomy

🌾📈 A comparison of the predictive capacity of empirical models and a process-based model for #wheat yield
by Achraf Mamassi, Marie Lang, Bernard Tychon, Mouanis Lahlou, Joost Wellens, Mohamed El Gharous & Hélène Marrou

https://buff.ly/466kQCU
#PlantScience 🧪 #APSIM

Insights from utilizing data of different quality levels for simulating barley performance under Nordic conditions: The Agricultural Production Systems SIMulator model evaluation Abstract. Crop model-aided ideotyping can accelerate the breeding of resilient barley cultivars. Yet, the accuracy of process descriptions in the crop mode

This post is based on the article Insights from utilizing data of different quality levels for simulating #barley performance under Nordic conditions using #APSIM by Mercy Appiah, Gennady Bracho-Mujica, Simon Svane, Merete Styczen, Kurt-Christian Kersebaum, Reimund P Rötter

https://buff.ly/3W6FZet

s based on the article Insights from utilizing data of different quality levels for simulating #barley performance under Nordic conditions using #APSIM by Mercy Appiah @BrachoMujicaG Simon Svane, Merete Styczen, Kurt-Christian Kersebaum @ReimundpRoetter

https://

Leveraging High-Quality Data for Improved Crop Simulations Researchers improve model prediction accuracy by closing existing data gaps.

🌾💻 Leveraging High-Quality Data for Improved Crop Simulations

Researchers improve #barley model prediction accuracy by closing existing data gaps using #APSIM

https://buff.ly/3BK9vyT via @Botany.One #PlantScience

Leveraging High-Quality Data for Improved Crop Simulations Researchers improve model prediction accuracy by closing ...

🌾💻 Leveraging High-Quality Data for Improved Crop Simulations

Researchers improve #barley model prediction accuracy by closing existing data gaps using #APSIM

https://buff.ly/3BK9vyT via @BotanyOne #PlantScience

NEW!
Changes in the leaf area-seed #yield relationship in #soybean driven by genetic, management and environments using #APSIM
from @MarianaVChiozza Kyle Parmley, William Schapaugh, Antonio Asebedo @drsinghak @FernandoMiguez1

https://buff.ly/3Y9xFfn

⭐ NEW⭐
Insights from utilizing data of different quality levels for simulating #barley performance under Nordic conditions: #APSIM model evaluation

by Mercy Appiah @BrachoMujicaG Simon Svane, Merete Styczen, Kurt-Christian Kersebaum @ReimundpRoetter

https://buff.ly/3W6FZet

A pyramid portraying quality levels of data suitable for different modeling tasks. At the bottom is model application. For this level, final yield is required and estimated management soil and weather data is desirable. Farm surveys are the source of this data. Next is calibration and evaluation. Yield components are required, anthesis and maturity dates, in season LAI/total aboveground biomass, and management inputs are desired. National variety trials are the source of this data. Next is comparison. On-site, rainfall data and all management inputs are required. Remaining weather data from nearby station and four in-season leaf area index/total above ground biomass/grain data and in-season tissue nitrogen are desired. This data comes from agronomic experiments. Finally, at the top, is improvement. On-site weather, soil, and leaf area index/total above ground biomass/green data required. Rooting depth is desired. The source of this data is specific high-quality field experiments.

⭐ NEW⭐
Insights from utilizing data of different quality levels for simulating #barley performance under Nordic conditions: #APSIM model evaluation
#PlantScience
by Mercy Appiah, Gennady Bracho-Mujica, Simon Svane, Merete Styczen, Kurt-Christian Kersebaum & Reimund Rötter
doi.org/10.1093/insi...

APSIM’s origins and the forces shaping its first 30 years of evolution: A review and reflections by Brian Keating @QAAFI

#APSIM #crop #soil #model
https://buff.ly/3Wcp36F via @ASD_INRAE

🌾📈 Predictive Modeling of #Wheat #Yields: A Comparison of Empirical and Process-Based Models

https://buff.ly/466kQCU from @AchrafMamassi Marie Lang, Bernard Tychon, Mouanis Lahlou, @JoostWellens Mohamed El Gharous and Hélène Marrou

#APSIM

New algorithm for pearl #millet modelling in #APSIM allowing a mechanistic simulation of tillers by Vincent Garin, Erik Van Oosterom, Greg McLean, Graeme Hammer, Tharanya Murugesan, Sivasakthi Kaliamoorthy, Madina Diancumba, et al

https://buff.ly/41ncsy0 via @biorxiv_plants

ICYMI 🌾 🧬
Adapting #sorghum sowing date and genotype maturity to seasonal #rainfall variation in a temperate region from Ana Carcedo, Emilia Cejas, Brenda Gambin #precipitation #APSIM

#OpenAccess 👉🏾 https://bit.ly/3YyjGN1