Modeling crop nitrogen requirements: A critical analysis

1 tables 5 graph. Special issue: Perspectives for agronomy. Adopting ecological principles and managing resource useInternational audienc

Comparison of CropSyst performance for water management in Southwestern France using submodels of different levels of complexity

International audienc

Comparison of CropSyst performance for water management in Southwestern France using submodels of different levels of complexity

3 tables 1 graph. Special issue: Perspectives for agronomy. Adopting ecological principles and managing resource useInternational audienc

Comprendre et prédire la phénologie du soja pour adapter la culture à de nouveaux environnements climatiques.

L’exploration de nouvelles stratégies agronomiques (semis très précoce, expansion de la culture à des latitudes plus élevées, double culture) pour augmenter la production de soja en Europe dans un contexte de changement climatique impose de prévoir la phénologie de la culture dans des environnements thermiques et photopériodiques variés. Dans cet objectif, le modèle (Simple Phenology) de prédiction de la phénologie du soja a été calibré et évalué à partir de données expérimentales. Pour la calibration, deux expériences ont été réalisées avec 10 génotypes contrastés (groupes de maturité 000 à II) : 1- suivi phénologique de plantes en pots sur la plateforme HeliaphenINRA Toulouse avec 6 dates de semis, 2- réponse de la germination à la température en conditions contrôlées. L’évaluation du modèle a été réalisée à partir d’essais multi-locaux menés dans le cadre du projet SOJAMIP 2012-15 ainsi qu’à l’INRA Toulouse en 2017 et 2018. Les températures cardinales de germination (Tmin, Topt et Tmax) sont proches de 0, 30 et 40°C ; avec des différences significatives de sensibilité des variétés à la photopériode. Le modèle calibré avec un paramétrage variétal a montré une RMSE de ~ 6 jours pour la prédiction du cycle cultural (i.e. stade cotylédons à maturité physiologique). Coupler l’expérimentation et la modélisation agronomique permettra de positionner le cycle cultural de variétés de soja dans de nouveaux environnements.Developing new cropping strategies (very early sowing, crop expansion at higher latitudes, double cropping) to improve soybean production in Europe under climate change needs a good prediction of phenology in different temperature and photoperiod conditions. For this aim, a soybean phenology model was calibrated and evaluated using experimental data. Two experiments were carried out with 10 contrasting genotypes (maturity group 000 to II): 1- Phenological monitoring of plants in pots on the Heliaphen platform with 6 sowing dates (INRA Toulouse). 2- Response of seed germination to temperature in controlled conditions. Multilocal trials carried out as part of the SOJAMIP 2012-15 project and at INRA Toulouse in 2017and 2018, were used to evaluate the phenology predicted by Simple [br/] Phenology (SP) model. Cardinal temperatures (minimal, optimal and maximal) of germination were close to 0, 30 and 40°C, respectively; with significant differences for photoperiod sensitivity among varieties. The calibrated model with varietal parameters showed an RMSE of less than 6 days for the prediction of crop cycle duration (i.e. cotyledons stage to physiological maturity). Combining experimentation and agronomic modeling will make it possible to predict phenology of soybean genotypes in new environments

Yield Response of an Ensemble of Potato Crop Models to Elevated CO2 in Continental Europe

A multi-model inter-comparison study was conducted to evaluate the performance of ten potato crop models to accurately predict potato yield in response to elevated CO2 (Ce) when calibrated with ambient CO2 data (Ca). Experimental data from seven open-top chambers (OTC) and free-air−CO2-enrichment (FACE) facilities across continental Europe were used. Model ensemble percent errors averaged over all datasets for simulated yields were 26.5 % for Ca and 27.2 % Ce data. Metrics such as Wilmott’s index of agreement (IA) and root mean square relative error (RMSRE) ranged broadly among individual models and locations, such that four of the ten models outperformed the median or mean of the ensemble for about half of the Ce datasets. These top performing models were representative of three different model structural groups, including radiation use efficiency, transpiration efficiency, or leaf-level based approaches. Relative response to an increase in CO2 was more accurately modeled than absolute yield responses when averaged across all locations, and within 3.3 kg ppm−1 (or 5%) of observed values. Specific targets in the model structure needed for improvement were not identified due to large and inconsistent variation in the accuracy of yield predictions across locations. However, models with the lowest calibration errors tended to be top performers for Ce predictions as well. Such results suggest calibration is at least as important as model structure. Where possible, modelers using potato models to estimate Ce responses should use Ce calibration data to improve confidence in such predictions

Precipitation downscaling in Canadian Prairie Provinces using the LARS-WG and GLM approaches

Two stochastic precipitation simulation models, namely the Long Ashton Research Station weather generator (LARS-WG) and a Generalized Linear Model-based weather generator (GLM-WG), are evaluated for downscaling daily precipitation at four selected locations (Banff, Calgary, Saskatoon and Winnipeg) in the Canadian Prairies. These weather generators model precipitation occurrence and amount components separately. Large-scale climate variables (including mean temperature, sea level pressure and relative humidity, derived from National Centers for Environmental Prediction reanalysis data) and observed precipitation records are used to calibrate and validate GLM-WG, while only observed precipitation records are used to calibrate and validate LARS-WG. A comparison of common statistical properties (i.e. annual/monthly means, variability of daily and monthly precipitation and monthly proportion of dry days) and characteristics of drought and extreme precipitation events derived from simulated and observed daily precipitation for the calibration (1961-1990) and validation (1991-2003) periods shows that both weather generators are able to simulate most of the statistical properties of the historical precipitation records, but GLM-WG appears to perform better than LARS-WG for simulating precipitation extremes and temporal variability of drought severity indices. For developing projected changes to precipitation characteristics, a change factor approach based on Canadian Global Climate Model (CGCM) simulated current (1961-1990) and future (2071-2100) period precipitation is used for driving simulations of LARS-WG, while for driving GLM-WG simulations, large-scale predictor variables derived from CGCM current and future period outputs are used. Results of both weather generators suggest significant increases to the mean annual precipitation for the 2080s. Changes to selected return levels of annual daily precipitation extremes are found to be both location- and generator-dependent, with highly significant increases noted for Banff with LARS-WG and for both Banff and Calgary with GLM-WG. Overall, 5- and 10-yr return levels are associated with increases (with the exception of Winnipeg) while 30- and 50-yr return levels are associated with site-dependent increases or decreases. A simple precipitation-based drought severity index suggests decreases in drought severity for the 2080s. © 2013 Canadian Water Resources Association

Statistical analysis of large simulated yield datasets for studying climate effects.

Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compared. See more from this Division: Special Sessions See more from this Session: Symposium--Perspectives on Climate Effects on Agriculture: The International Efforts of AgMI

Water deficit and nitrogen nutrition of crops. A review

Among the environmental factors that can be modified by farmers, water and nitrogen are the main ones controlling plant growth. Irrigation and fertilizer application overcome this effect, if adequately used. Agriculture thus consumes about 85% of the total fresh water used worldwide. While only 18% of the world’s cultivated areas are devoted to irrigated agriculture, this total surface represents more than 45% of total agricultural production. These data highlight the importance of irrigated agriculture in a framework where the growing population demands greater food production. In addition, tighter water restrictions and competition with other sectors of society is increasing pressure to diminish the share of fresh water for irrigation, thus resulting in the decrease in water diverted for agriculture.The effect of water and nutrient application on yield has led to the overuse of these practices in the last decades. This misuse of irrigation and fertilizers is no longer sustainable, given the economic and environmental costs. Sustainable agriculture requires a correct balance between the agronomic, economic and environmental aspects of nutrient management. The major advances shown in this review are the following: (1) the measurement of the intensity of drought and N deficiency is a prerequisite for quantitative assessment of crop needs and management of both irrigation and fertilizer application. The N concentration of leaves exposed to direct irradiance allows both a reliable and high-resolution measurement of the status and the assessment of N nutrition at the plant level. (2) Two experiments on sunflower and on tall fescue are used to relate the changes in time and irrigation intensity to the crop N status, and to introduce the complex relationships between N demand and supply in crops. (3) Effects of water deficits on N demand are reviewed, pointing out the high sensitivity of N-rich organs versus the relative lesser sensitivity of organs that are poorer in N compounds. (4) The generally equal sensitivities of nitrifying and denitrifying microbes are likely to explain many conflicting results on the impact of water deficits on soil mineral N availability for crops. (5) The transpiration stream largely determines the availability of mineral N in the rhizosphere. This makes our poor estimate of root densities a major obstacle to any precise assessment of N availability in fertilized crops. (6) The mineral N fluxes in the xylem are generally reduced under water deficit and assimilation is generally known to be more sensitive to water scarcity. (7) High osmotic pressures are maintained during grain filling, which enables the plant to recycle large amounts of previously assimilated N. Its part in the total grain N yield is therefore generally higher under water deficits. (8) Most crop models currently used in agronomy use N and water efficiently but exhibit different views on their interaction