Soil nitrogen mineralisation simulated by crop models across different environments and the consequences for model improvment. iCROPM2016 International Crop Modelling Symposium

Crop models are the state-of-the-art tool to predict crop yields in the context of climate change and food security. The uncertainty associated with their use can be partly overcome by using multi-model ensembles (mme), though model improvement

Prior to their use in decision-making, crop models need to be calibrated with field data from the region where the model will be used. The objectives of this research were calibrate and validate the predictive ability of CROPGRO-soybean model. Data from two cultivars maturity group IV (Asgrow 4656 and Don Mario DM4700) and three years of field experiments in conditions not limiting in Azul, Buenos Aires, Argentina were used. Calibration started with the coefficients of group IV that by default are in files: species, ecotype and cultivate. With the original species file, dry matter, increasing the number of pods and growth were underestimated. Minor changes in file were made to adjust phenology and growth dynamics for both cultivars. The cardinal base temperatures for photosynthesis and pod formation were reduced; with these modifications good predictions for growth and yield were obtained. With the CROPGRO-soybean calibrated and using projections for the region from PRECIS regional climate model under the SRESA2 scenario in the years 2030 and 2060, the effects of global climate change in future soybean crop yield were evaluated. Under these scenarios and rainfed conditions, are anticipated yield increases of 34 and 38% for each of the years studied with a slight improvement delaying the planting date, respect to current optimum date.

Antes de su uso para la toma de decisiones, los modelos de cultivos deben ser calibrados con datos de campo de la región en la cual serán utilizados. Los objetivos del trabajo fueron calibrar y validar la capacidad predictiva del modelo CROPGRO-soybean. Se utilizaron datos de dos cultivares del grupo de madurez IV (Asgrow4656 y Don Mario DM4700) y tres años de experimentos de campo en condiciones no limitantes en Azul, Buenos Aires, Argentina. La calibración comenzó con los coeficientes del grupo IV que por defecto se encuentran en los archivos: especie, ecotipo y cultivar. Modificaciones menores fueron hechas para ajustar la fenología y dinámica del crecimiento para ambos cultivares. Con el archivo especie original la materia seca, el aumento del número de vainas y el crecimiento de las mismas fue subestimado. La temperatura base cardinal para la fotosíntesis y formación de vainas se redujeron, con estas modificaciones se obtuvieron buenas predicciones para crecimiento y rendimiento. Con el CROPGRO-soybean calibrado y utilizando las proyecciones para la región del modelo climático regional PRECIS bajo el escenario SRESA2 en los años 2030 y 2060, se evaluaron los efectos del cambio climático global futuro en los rendimiento del cultivo de soja. Bajo esos escenarios y en condiciones de secano, se prevén aumentos del rendimiento de 34 y 38% para cada uno de los años estudiados, con una leve mejora atrasando la fecha de siembra respecto de la óptima actual.

Crop models should accurately predict soil water balance, evapotranspiration (ET), and water deficit effects on growth and development processes and ultimately yield. We (i) describe the soil water balance and ET options in the CROPGRO model; (ii) illustrate how water stress effects are implemented on processes of photosynthesis–transpiration, leaf expansion, internode elongation, assimilate partitioning, pod addition, vegetative node expression, and reproductive development; (iii) document the dynamic growth responses of the CROPGRO model to water deficit; (iv) highlight tests of soil water balance and ET that justify recent changes and future needed improvements; and (v) explore new ways of modeling crop perception of water deficit that translate into better prediction of responses observed in literature. We conclude the Ritchie tipping bucket soil water balance model in DSSAT (Decision Support System for Agrotechnology Transfer software; Jones et al., 2003) generally works satisfactorily when the soil water-holding traits (drained upper limit, DUL; lower limit of plant-extractable soil water, LL) are estimated properly and when root growth is adequately predicted. Of the potential evapotranspiration (PET) options, the Priestley-Taylor option is the default because it does not require windrun or dewpoint. We find the Priestley-Taylor option predicts ET satisfactorily when the extinction coefficient (total solar energy extinction coefficient, KEP) for partitioning PET to transpiration is reduced from 0.85 to 0.70 for all CROPGRO V4.0 crops and from 1.00 to 0.68 for CERES-Maize. When windrun and dewpoint are available, then the FAO-56 PET option is generally best, while the FAO-24 PET option overpredicts ET. Where the soil water balance is adequately predicted, the crop water stress signals, soil water stress factor (SWFAC), and plant turgor factor (TURFAC) appear to function well to modify crop assimilation, expansive growth processes, and crop development. Simulated comparisons with field data illustrate how these signals act to enhance partitioning to root, reduce leaf area, and accelerate crop maturity. Overall, CROPGRO satisfactorily simulates water deficit effects on growth and development processes, although crop-specific signals that act sooner than the current TURFAC are needed to accelerate or delay the onset of reproductive growth.

AgMIP is an international program bringing together research projects on climate, crop modelling and regional agriculture adaptation to climate change. One objective is to better assess the projections of global food availability depending on different staple crops (wheat, rice and maize), taking into account the projections of climate change for the end of century and the uncertainty attached to them. The need for robust estimates, i.e. good crop models for yields and use of natural resources is a prerequisite to benchmark the various cropping systems and local solutions that will ultimately be explored in order to cope with climate change, without bringing about any negative side effects on the environment. Modelers hence work together internationally in order to compare and improve process-based crop simulation models. Maize is a strategic crop, exhibiting high potential radiation and water use efficiencies and is cultivated worldwide. In a first phase, the impacts of CO2 and temperature on the maize yields and water use were studied using 23 crop models on 4 sites with contrasted cool or hot climate conditions, under no water limitation (Lusignan in France, Ames in the United States, Morogoro in Tanzania and Rio Verde in Brasil). Models were run using local soil conditions and climate variables for 30 years (1980-2010) after adjusting the cultivar parameters to the ones used in one experiment in each site. At the four sites studied, the average values across models of simulated yields were closer to the observed local experimental results than the simulation of any individual model. This indicated that ensemble modelling could be a relevant way to approach the impact of climate change on maize yields. There was also a broad agreement between models to simulate a reduction in maize yield in response to temperature, roughly - 0.5 Mg ha-1 per °C increase, with no significant impact on water use, although the latter variable was estimated with a large variability between models. Plant phenology was the mostly altered process with increasing temperature. Shortening of the duration from flowering to maturity in particular reduced the gain in grain weight during that phase. This suggests that genetics could hence play a key role in adapting maize production to climate change, at least under high water availability. Doubling [CO2] from 360 to 720 μmole mole-1 increased grain yield by 7.5% on average across models and sites, with a slight decrease of water use, bringing about an increase in water use efficiency. However, the variability of the response to [CO2] was very high, bringing about the need to better simulate the role of CO2, especially on plant transpiration. In a second phase, models are therefore now being tested against Free Air CO2 Enrichment experimental data, so that variability can be reduced and the actual impact of global change on water use can be assessed with a relevant precision to adaptating agricultural practices. (Texte integral)

AgMIP is an international program bringing together research projects on climate, crop modelling and regional agriculture adaptation to climate change. One objective is to better assess the projections of global food availability depending on different staple crops (wheat, rice and maize), taking into account the projections of climate change for the end of century and the uncertainty attached to them. The need for robust estimates, ie good crop models for yields and use of natural resources is a prerequisite to benchmark the various cropping systems and local solutions that will ultimately be explored in order to cope with climate change, without bringing about any negative side effects on the environment. Modelers hence work together internationally in order to compare and improve process-based crop simulation models. Maize is a strategic crop, exhibiting high potential radiation and water use efficiencies and is cultivated worldwide. In a first phase, the impacts of CO2 and temperature on the maize yields and water use were studied using 23 crop models on 4 sites with contrasted cool or hot climate conditions, under no water limitation (Lusignan in France, Ames in the United States, Morogoro in Tanzania and Rio Verde in Brasil). Models were run using local soil conditions and climate variables for 30 years (1980-2010) after adjusting the cultivar parameters to the ones used in one experiment in each site. At the four sites studied, the average values across models of simulated yields were closer to the observed local experimental results than the simulation of any individual model. This indicated that ensemble modelling could be a relevant way …

Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. (C) 2015 Elsevier B.V. All rights reserved.

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

The germplasm pear bank in north-western Spain includes 221 accessions collected in the region between 1978 and 1981. Four reconstructed populations (RPPs) were detected by simple sequence repeats (SSRs) using a Bayesian method, two of which included French and English cultivars introduced to north-western Spain after reconquest by the Muslims in the 10th century. We studied 22 morphological and 15 phenological characteristics. We used principal component and cluster analysis to identify and classify the main origins of variability, these being flowering time, size and shape of fruit, time of harvesting, percentage of russeting and firmness of the flesh. Genetic differentiation between reconstructed populations, detected by a Bayesian method using SSRs, was also found in morphology, though with a great diversity inside each RPP that overlapped the main RPPs. This was consistent with the excellent quality of the reference cultivars apparently involved in the origin of the two main RPPs, 'Mantecosa Hardy' and 'Williams', suggesting that the contribution of foreign cultivars and subsequent selection by farmers have led to common phenotypic characteristics in both main groups. The introduction of cultivars from France and the United Kingdom could have occurred before 1746, when the first references to local cultivars related to both main RPPs were made. Furthermore, hybridisation with local species could explain part of the variability detected, which can be exploited for breeding programmes and local productions, nowadays focused on a reduced number of cultivars. (C) 2012 Elsevier B.V. All rights reserved.

Predicting the phenology of faba bean is a critical step to designing good crop management practices, as well as to the development of crop models. This arti...

Models may be useful tools to design efficient crop management practices provided they are able to accurately simulate the effect of weather variables on crop performance. The objective of this work was to accurately simulate the effects of temperature and day length on the rate of vegetative node expression, time to flowering, time to first pod, and time to physiological maturity of faba bean (Vicia faba L.) using the CROPGRO‐Fababean model. Field experiments with multiple sowing dates were conducted in northwest Spain during 3 yr (17 sowing dates: 12 used for calibration and five for validation). Observed daily minimum and maximum air temperatures were within the range of –9.0 and 39.2°C and observed photoperiods within 10.1 to 16.6 h. Optimization of thermal models to predict leaf appearance raised the base temperature (Tb) from the commonly used value of 0.0 to 3.9°C. In addition, photothermal models detected a small accelerating effect of day length on the rate of leaf appearance. Accurate prediction of the flowering date required incorporating day length, but the solved Tb approached negative values, close to –4°C. All the reproductive phases after flowering were affected only by temperature, but postanthesis Tb was also >0°C and approached values close to 8°C for time to first pod set and 5.5°C for time from first pod to physiological maturity. Our data indicated that cardinal base temperatures are not the same across all phenological phases.

Four cool season grain legume genotypes -Vicia faba L. cultivars Alameda, Irena and Diva and a line of Vicia narbonensis- were evaluated in Lugo (43o04’ N, 7o30’ O, 480 m elevation, Galicia, Spain), for their potential as grain and forage crops at an early sowing date. The results indicate that faba bean cultivars ‘Diva’ and ‘Irena’, with longest crop cycle, had the highest grain yield. However, Vicia narbonensis L. can be a good crop alternative for early forage production, as it produces relatively high quantities of good quality forage in early may.

espanolEntre los anos 1997 y 2002, se ha estudiado en Lugo el rendimiento en regadio y en secano de la rotacion anual raigras italiano alternativo-maiz forrajero bajo dos tecnicas de siembra: laboreo convencional y siembra directa. Se incluyo en el diseno experimental una pradera de corta duracion de raigras italiano no alternativo, para contrastarla con las producciones de la rotacion intensiva. Considerando la rotacion de dos cultivos por ano, las producciones medias de los cinco anos estudiados, se situaron en 28,00, 21,67, 27,93 y 20,90 t ha-1 de materia seca (MS) (laboreo regadio, laboreo secano, siembra directa regadio y secano, respectivamente). En el caso del raigras no alternativo se situaron en 15,69 y 8,27 t ha-1 de MS (regadio y secano respectivamente). Siendo como se ve, los rendimientos de la rotacion mas intensiva muy superiores a los de raigras no alternativo, incluso en condiciones de secano supera al raigras no alternativo en regadio en un 36% (promedio de los cinco anos). Dentro de la rotacion raigras italiano alternativo-maiz, en la mayor parte de los anos ensayados no se detectaron diferencias atribuibles al sistema de siembra, lo que parece mostrar que la tecnica de siembra no afecta a la produccion de esta rotacion. Las producciones de los tratamientos regados han superado ampliamente a los secanos. Dentro de la rotacion raigras alternativo-maiz, esto fue debido a las mayores producciones del maiz en regadio, ya que las producciones del raigras alternativo precedidas de maiz secano fueron superiores en cuatro de los anos ensayados a las precedidas de maiz regadio. En el raigras no alternativo las producciones de los tratamientos regados tambien han sido superiores. Incluso en el verano de 1998, el raigras no alternativo en secano se seco completamente, no rebrotando el otono siguiente, con lo que la produccion este ano fue nula. Palabras clave: Lolium multiforum L., Zea mays L., rotaciones forrajeras, siembra directa. EnglishBiomass production of a ryegrass-forage maize rotation was studied in Lugo (northwest Spain), between 1997 and 2002, under irrigated and rainfed conditions and two sowing systems, conventional sowing and direct drilling. The experiment included a short-duration biannual ryegrass to compare with the intensive forage rotation. Average fve-year dry matter (MS) forage yields of the intensive rotation (two crops per year) were 28.00, 21.67, 27.93 and 20.90 t ha-1 for irrigated conventional sown, rainfed conventional sown, irrigated direct drilled, and rainfed direct drilled plots, respectively. Average MS forage yields of the biannual ryegrass were 15.69 and 8.27 t ha-1 for irrigated and rainfed treatments. Forage yields of the more intensive rotation were much higher than those of the biannual ryegrass. Even the biomass production of the intensive rotation (fve year average) under rainfed conditions, was 36% higher than biomass of the irrigated biannual ryegrass. Biomass yield of the ryegrass-forage maize rotation was not affected by the sowing system in most of the experimental years. The forage yields of irrigated treatments were much higher than those of rainfed treatments. For the ryegrass-forage maize rotation, this was due to the large production of irrigated maize. In four out of fve years, ryegrass production after rainfed maize was higher than after irrigated maize. For the biannual ryegrass, forage yields of irrigated treatments were also higher than yields of rainfed treatments. The rainfed biannual ryegrass did not recover from the frst year summer drought in 1998 and there was no fall re-growth, thus the second year forage production was zero

Predicting the phenology of faba bean is a critical step to designing good crop management practices, as well as to the development of crop models. This article examines the phenological response of faba bean (Vicia faba L.) to different temperature and photoperiod regimes assessed through linear models. Seventeen field-sowing dates were used over a three-year lapse in Lugo, Spain (43 degrees 04' N, 7 degrees 30' W, 480 m altitude), on which phenological observations were made. The time from emergence to flowering was satisfactorily described using a photothermal model, whereas the rates of progress from sowing to flowering, flowering to first pod and first pod to physiological maturity were satisfactorily modeled using only temperature as independent variable. Basal temperature values ranged between 2.09 and 4.47 degrees C, depending on the phenological subperiod. Base photoperiod was 6.9 h, while the critical photoperiod was 16.2 h.

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