The Global Gridded Crop Model Intercomparison: Data and modeling protocols for Phase 1 (v1.0)

We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project includes global simulations of yields, phenologies, and many land-surface fluxes using 12–15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record

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

Soil Organic Carbon and Nitrogen Feedbacks on Crop Yields under Climate Change

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Physiology and modelling of traits in crop plants: implications for genetic improvements

Crop growth models have excellent potential-for evaluating genetic improvement, for analyzing past genetic improvement from experimental data, and for proposing plant ideotypes for target environments. Crop models used for these plant breeding applications should be sufficiently mechanistic that processes can be investigated in a manner familiar to crop physiologists and plant breeders. In addition, the crop models must consider a sufficient number of cultivar-specific traits descriptive of life cycle phases, vegetative traits, and reproductive growth attributes. In this paper, we discuss how crop models consider genetic variability within a species (cultivar variation), how varietal characteristics can be determined from variety trial or other data, how crop models can be used to evaluate past genetic improvement, and how crop models can be used to hypothesize ideotypes for specific environments. We conclude that crop growth models can partially reproduce genotype by environment interactions when considered across broad ranges of weather and sites, and that crop models can be used to help plant breeders target cultivar improvement for specific environments. However, more physiological insight into primary processes such as source-sink relationships and morphological development will be needed for enhanced application of the models in breeding programmes. ? 2001 Elsevier Science Ltd. All rights reserve

Improving the CROPGRO-Tomato model for predicting growth and yield to temperature

Parameterizing crop models for more accurate response to climate factors such as temperature is important considering potential temperature increases associated with climate change, particularly for tomato (Lycopersicon esculentum Mill.), which is a heat-sensitive crop. The objective of this work was to update the cardinal temperature parameters of the CROPGRO-Tomato model affecting the simulation of crop development, daily dry matter (DM) production, fruit set, and DM partitioning of field-grown tomato from transplanting to harvest. The main adaptation relied on new literature values for cardinal temperature parameters that affect tomato crop phenology, fruit set, and fruit growth. The new cardinal temperature values are considered reliable because they come from recent published experiments conducted in controlled-temperature environments. Use of the new cardinal temperatures in the CROPGRO-Tomato model affected the rate of crop development compared with prior default parameters; thus, we found it necessary to recalibrate genetic coefficients that affect life cycle phases and growth simulated by the model. The model was recalibrated and evaluated with 10 growth analyses data sets collected in field experiments conducted at three locations in Florida (Bradenton, Quincy, and Gainesville) from 1991 to 2007. Use of modified parameters sufficiently improved model performance to provide accurate prediction of crop and fruit DM accumulation throughout the season. Overall, the average root mean square error (RMSE) over all experiments was reduced 44% for leaf area index, 71% for fruit number, and 36% for both aboveground biomass and fruit dry weight simulations with the modified parameters compared with the default. The Willmott d index was higher and was always above 0.92. The CROPGRO-Tomato model with these modified cardinal temperature parameters will predict more accurately tomato growth and yield response to temperature and thus be useful in model application