IXIM: A new maize simulation model for DSSAT v4.5.

The Decision Support System for Agrotechnology Transfer (DSSAT) is a suite of crop simulation models and associated tools for simulating growth, development, and yield of 25 crops. The maize simulation model in DSSAT is CSM-CERES, the modular version of CERES-Maize, which was first published in 1986. The newest release of DSSAT, version 4.5, provides users with the opportunity to run an alternative maize simulation model. IXIM (eeh-sheem), the Mayan language for maize, is a new, more mechanistic, maize simulation model fully compatible with DSSAT. The purpose of this work is to compare seasonal simulations of maize growth and N uptake using CSM-CERES and IXIM

Predicting phenology of Vicia faba: Parameter estimation with CROPGRO-fababean model using multiple sowing date experiments.

Crop models have become valuable tools for designing efficient cropping systems, particularly once model reliability is documented for a given environment. For this use, the timing of crop phenology has to be accurately simulated to predict life cycle and the correct allocation of assimilates to yield components. The CROPGRO-Fababean model was developed based on adaptation of the generic CROPGRO legume model to simulate faba bean grown in Cordoba, Spain (Boote et al., 2002) but the model has not been tested extensively in other environments. Therefore, the model needs to be tested for additional environments, and may need to be modified to improve its reliability under a wide range of field conditions. For the initial model version, phase durations were calibrated against field data collected at Córdoba; however, the cardinal temperatures that affect phenology were derived from the literature. Because our goal was to use these parameters to make reliable predictions in new field environments, we propose that the best way to solve the coefficients is through a calibration process based on field data obtained under varying daily and seasonal temperature and daylength, similar to the method used successfully to calibrate the SOYGRO model phenology. The objective of this work was to determine quantitatively the effects of temperature and daylength on rate of vegetative node expression, time to flowering, time to beginning pod, time to beginning seed, and time to physiological maturity with the ultimate goal of making the CROPGRO-Faba bean model more reliable over a wide range of sowing date environments

Predicción de la fenología de vicia faba l.: estimación de parámetros con el modelo cropgro- faba bean usando experimentos de múltiples fechas de siembra.

Entre los modelos de leguminosas más mecanicistas se puede destacar el modelo CROPGRO. Boote et al. (2002) adaptaron el CROPGRO para simular el crecimiento del haba (Vicia faba L.), naciendo así, CROPGRO-faba bean (incluido en el paquete DSSAT V4) en el que la tasa de desarrollo se expresa como día fisiológico (DF) transcurrido por día del calendario (día) (Ec. 1) y es una función multiplicativa de la temperatura (T) y fotoperíodo (P). Cada una de estas funciones adopta valores comprendidos entre 0 y

AgMIP Training in Multiple Crop Models and Tools

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter

Assessing Agricultural Risks of Climate Change in the 21st Century in a Global Gridded Crop Model Intercomparison

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies

An AgMIP framework for improved agricultural representation in integrated assessment models

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications