Academic literature on the topic 'Crop modelling, durum wheat, climate change, ideotype'

Durum wheat is a major crop in the Mediterranean basin, where water deficit is the most important factor affecting its production. Under drought conditions, the root system has a crucial role in crop productivity as a water and nutrition supplier. The aim of the study was to analyze root system diversity in six contrasting durum wheat accessions, including two hydric stress-tolerant genotypes, and to evaluate root traits using the high-throughput phenotyping scanner Win-RHIZO in order to determine the main traits to be used in breeding programs. Six durum wheat accessions were subjected to two drought events under greenhouse conditions from the seedlings stage (BBCH12) for 49 days. Root phenotyping data were validated with results from plants grown in the rainfed field. This study highlighted a great variability among the analyzed genotypes in terms of development, distribution, and architecture of the root system under difficult environments, underlining a good resilience to climate change. Interestingly, the two hydric stress-tolerant genotypes, Cham1 and J. Khetifa, showed different root system ideotypes and rooting patterns under drought conditions. The late flowering landrace J. Khetifa (as also genotypes; Pelsodur and Vulci) showed a steep and long root system ideotype that led to the maintaining of the highest root biomass, length, and volume under drought conditions, while the early flowering genotype Cham1 (as also genotype; Sebatel) was distinguished by a wider root system ideotype, and by increasing the root volume in the topsoil as a strategy to tolerate drought. Moreover, a significant positive correlation was obtained between the root angle of plants grown under greenhouse conditions and plants from the field. Our results demonstrated that screening plant roots in early stages grown under greenhouse conditions using high-throughput phenotyping systems can speed up the selection for crop improvement and future drought stress breeding programs.

With an approach combining crop modelling and biotechnology to assess the performance of three durum wheat cultivars (Creso, Duilio, Simeto) in a climate change context, weather and agronomic datasets over the period 1973–2004 from two sites, Benatzu and Ussana (Southern Sardinia, Itay), were used and the model responses were interpreted considering the role of DREB genes in the genotype performance with a focus on drought conditions. The CERES-Wheat crop model was calibrated and validated for grain yield, earliness and kernel weight. Forty-eight synthetic scenarios were used: 6 scenarios with increasing maximum air temperature; 6 scenarios with decreasing rainfall; 36 scenarios combining increasing temperature and decreasing rainfall. The simulated effects on yields, anthesis and kernel weights resulted in yield reduction, increasing kernel weight, and shortened growth duration in both sites. Creso (late cultivar) was the most sensitive to simulated climate conditions. Simeto and Duilio (early cultivars) showed lower simulated yield reductions and a larger anticipation of anthesis date. Observed data showed the same responses for the three cultivars in both sites. The CERES-Wheat model proved to be effective in representing reality and can be used in crop breeding programs with a molecular approach aiming at developing molecular markers for the resistance to drought stress.

The modeling of carbon (C) and nitrogen (N) fluxes between microorganisms and plants in pure and associated cultures of durum wheat and faba bean demonstrated a close link between the C and N cycles in agroecosystems. The MOMOS (microorganisms and organic matter of soils) model integrates simplified descriptions of photosynthesis (origin of organic C in soil), N microbial exchange (soil origin for N), N fixation (atmospheric origin for N), and plant growth with an organic matter decomposition core that has the soil microbial community at its center. This work provides estimates of the exchange parameters between plant organs and microbes, which were compared to literature data when available. In a connection with photosynthesized C, the root demand for inorganic N can be adjusted by its microbial production. Our approach is a new methodology for improving plant production, by optimizing the interactions with soil microorganisms. Additionally, the coupling of plant growth and microbial processes enabled determining changes of the organic compartments of soil. In the unfertilized limestone soil of this study, sequestration was found to be located in the labile microbial metabolites for one year, then significantly transferred to stable humus during 6-year intercropping. Thus, we propose the MOMOS mathematical tool, not only for guiding ecological intensification, but also related to the management of agroecosystems for climate change mitigation.

Food and feed production must be increased or maintained in order to meet the demands of the earth's population. Under this scenario, the question that arises is how to address the demand for agricultural products given that the pressures on land use have already increased. In addition, it is obvious that climate change will have a serious negative impact and threaten the productivity and sustainability of food production systems. Therefore, understanding and predicting the outcome of crop production, while considering adaptation and sustainability, is essential. The need for information on decision making at all levels, from crop management to adaptation strategies, is constantly increasing and methods for providing such information are urgently needed in a relatively short period of time. Thus arises the need to use effective data, such as satellite and meteorological data, but also operational tools, to assess crop yields over local, regional, national, and global scales. In this work, three modeling approaches built on a fusion of satellite-derived vegetation indices, agro-meteorological indicators, and crop phenology are tested and evaluated in terms of data intensiveness for the prediction of wheat yields in large scale applications. The obtained results indicated that medium input data intensity methods are effective tools for yield assessments. The methods, namely, a semi-empirical regression model, a machine learning regression model, and a process-based model, provided high to moderate accuracies by fully relying on freely available datasets as sources of input data. The findings are comparable with those reported in the literature for detailed field experiments, thereby introducing a promising framework that can support operational platforms for dynamic yield forecasting, operating at the administrative or regional unit scale.

AbstractHow agricultural ecosystems adapt to climate change is one of the most important issues facing agronomists at the turn of the century. Understanding agricultural ecosystem responses requires assessing the relative shift in climatic constraints on crop production at regional scales such as the temperate zone. In this work we propose an approach to modeling the growth, development and yield of Triticum durum Desf. under the climatic conditions of north-eastern Poland. The model implements 13 non-measurable parameters, including climate conditions, agronomic factors, physiological processes, biophysical parameters, yield components and biological yield (latent variables), which are described by 33 measurable predictors as well as grain and straw yield (manifest variables). The agronomic factors latent variable was correlated with nitrogen fertilization and sowing density, and biological yield was correlated with grain yield and straw yield. An analysis of the model parameters revealed that a one unit increase in agronomic factors increased biological yield by 0.575. In turn, biological yield was most effectively determined by climate conditions (score of 60–62) and biophysical parameters (score of 60–67) in the 2nd node detectable stage and at the end of heading. The modeled configuration of latent and manifest variables was responsible for less than 70% of potential biological yield, which indicates that the growth and development of durum wheat in north-eastern Europe can be further optimized to achieve high and stable yields. The proposed model accounts for local climate conditions and physiological processes in plants, and it can be implemented to optimize agronomic practices in the cultivation of durum wheat and, consequently, to expand the area under T. durum to regions with a temperate climate.

Among the management practices that have a major role in determining the final yield of durum wheat, both in terms of quantity and in terms of quality, the choice of sowing date and variety are the most important. For this reason, SiriusQuality was applied in each selected location to investigate the genotype x environment x management interaction and to identify the sowing window able to optimize the yield. The advance of the sowing window compared to the traditional one, allowed to obtain a higher yield at the expense of the grain quality. Furthermore, at all locations, Karim variety was the most productive. In literature it is well known that wheat is a culture particularly sensitive to climate stress events, especially if these occur during more sensitive phenological phases such as flowering and grain filling. To investigate the impact of climate change in both medium (2050-2070) and far (2070-2090) future, SiriusQuality was applied in Florence, Foggia, Santaella and Sidi El Aydi. The impact of future climate change was assessed considering the frequency and the intensity of the occurrence of three climate stress events for wheat. The results showed the impact of climate change will have spatial differences and it will be related to the used scenario. Positive effects, in terms of quantity of grain, were simulated in Florence for all scenarios and in Foggia only for the medium future. While, negative effects are expected in Santaella and Sidi El Aydi for all scenarios. In general, the increase in the occurrence of climate stress events was more pronounced especially during the first 10 days after anthesis. Considering the impact of climate change and the need to optimize durum wheat yield, SiriusQuality was used to identify wheat ideotypes able to maximize yield and reduce inter-annual yield variation coefficient during the medium future (2050) under two climate scenarios, GISS and HadGEM. For same locality and for the same climate scenario, the identified wheat ideotypes are described by different sets of varietal parameters, all capable of maximizing yield and reducing inter-annual yield variability. The results of this study can be useful to understand the intensity of future climate change impact in the medium and in the far future in the Mediterranean basin. Furthermore, they can suggest which varietal characteristics can be improved to increase wheat yield in a climate change context.