Identifying plant traits to increase wheat yield under irrigated conditions.

Crop models have frequently been used to identify desired plant traits for rainfed wheat (Triticum aestivum L.). However, efforts to apply these models to irrigated wheat grown under non-limiting water and nitrogen conditions have been rare. Using simulation models to identify plant traits that impact yield can facilitate more targeted cultivar improvement and reduce time and cost. In this study, the SSM-iCrop model was employed to identify effective plant traits for increasing the yield of irrigated wheat in four distinct environments in Iran. A comprehensive range of traits related to phenology, leaf area development, dry matter production, and yield formation, which exhibited reported genetic variation, were tested. The impact of these traits on yield showed slight variation across different environmental zones due to genetic × environment interaction. However, across all environments, modifying current cultivars to increase radiation use efficiency (RUE) resulted in a 19 % increase in yield, accelerating leaf area development led to a 10 %-15 % increase, lengthening the grain filling period resulted in a 14 % increase, and extending the vegetative period led to a 6 % increase. These improvements were all statistically significant. Considering that longer duration cultivars may disrupt cropping systems and the need to develop simple methods for targeting and phenotyping RUE, faster leaf area development was found as the most promising option to increase irrigated wheat yield under optimal water and nitrogen management within a short time frame. It should be noted that cultivars with modified traits needed higher water and nitrogen inputs to support increased yields. These findings can be applied to select desirable key traits for targeted breeding and expedite the production of high-yielding cultivars of irrigated wheat in various environmental zones. The potential for further improvement through combined traits requires further investigation.

Predicting the effects of climate change on physiological parameters determining wheat yield in 2050 (case study: Golestan Province, Iran).

Climate change is one of the most important threats to food security. Earth's temperature is reported to increase by 1.5 to 4 °C by the end of the twenty-first century, compared to the base period (1850-1900), and will continue after 2100. Different models have been used to investigate the effects of climate change on different plant responses, including the exponential downscale statistical model of SDSM. Photosynthesis, respiration, and production are some of the first components to be affected by temperature which are discussed here. This study was aimed to introduce and compare the best interpolation method of main temperatures and precipitation to simulate the rate of photosynthesis, total respiration (total growth and maintenance respiration), and dry matter production of wheat in Golestan Province under climate change. Long-term data of 38 synoptic meteorological stations were used to interpolate the main temperature variables and provide reliable maps. Then, temperature change (ΔT) was used to simulate photosynthesis, total respiration, and dry matter production using the canopy photosynthesis simulation model (Can_Phs). The results clearly showed that by changing the minimum temperature by 1.1 to 3.1 °C and the maximum temperature by 2.3 to 4 °C, the amount of wheat production in the study area will be affected in 2050. This increase in temperature can reduce the length of the growing season in autumn wheat and limit the duration of intercepting light and capturing other resources, which in turn leads to a decrease in photosynthesis and increased respiration during the growing season.

Assessing the sensitivity of alfalfa yield potential to climate impact under future scenarios in Iran.

Alfalfa is a major forage crop in Iran. To quantify the impact of climate change on its yield and water application for irrigation in Iran, the SSM-iCrop2 simulation model and two GCMs of IPSL and HadGEM were used under RCP4.5 and RCP8.5 for the 2050s. Despite increased temperatures, alfalfa forage yield will increase in most of the regions across the country due to acceleration of spring regrowth, a higher number of cuttings, increased incident and received photosynthetically active radiation because of increased growing season length due to increased temperatures, and positive effect of CO2 on photosynthesis and radiation use efficiency. Changes in climatic conditions have had a significant impact on alfalfa net irrigation water, and the sum of net irrigation water has a direct relationship with alfalfa yield. Due to increased temperature, changes in rainfall, and improved concentration of atmospheric CO2, the forage yield of alfalfa will fluctuate highly under all climatic scenarios. The highest increase and decrease in the average yield using the HadGEM model under RCP8.5 was 32 and - 33%, respectively. The average net irrigation water of alfalfa increased by 36% in the HadGEM model under RCP8.5 and decreased by - 41% in the IPSL model under RCP8.5. Therefore, to improve alfalfa yield in Iran in the future, strategies compatible such as high temperature-tolerant cultivars may be the most reasonable approaches.

Ecological footprints of environmental resources for agricultural production in Iran: a model-based study.

A modeling system was used to calculate the resource footprints (land, water, nutrients, energy, fuel, electricity, and carbon) on a large scale in agricultural production systems (Iran as a case study), and this report is an introduction of this modeling system for future studies. Under irrigated conditions, the highest land footprint was observed in pulses and oil grains (0.6 ha t-1). The lowest water footprints were found in silage corn (300 m3 t-1), and the highest water footprints were observed in oil grains (4525 m3 t-1). The highest footprints of nitrogen were observed in maize (31.7 kg t-1), wheat (30.9 kg t-1), and oil grains (30.4 kg t-1), and the lowest value belonged to production of sugar crops (2.6 kg t-1). Most of the energy, fuel, electricity, and greenhouse gas (GHG) emissions were occurred under irrigated cropping systems compared with the rainfed systems. Under irrigated conditions, the highest footprints of energy, fuel, and electricity and GHG emissions occurred in the production of oil grains, and their values were 24397 MJ t-1, 161 L t-1, 1195 kWh t-1, and 1699 kg CO2eq. t-1, respectively. In general, wheat production in Iran has the highest cost in terms of resource use (water, elements, energy, and carbon) compared with the other plant products. Livestock and poultry products (especially red meat) also had the highest ecological footprint among the products.

Mapping Water Stress Incidence and Intensity, Optimal Plant Populations, and Cultivar Duration for African Groundnut Productivity Enhancement.

Groundnut production is limited in Sub-Saharan Africa and water deficit or "drought," is often considered as the main yield-limiting factor. However, no comprehensive study has assessed the extent and intensity of "drought"-related yield decreases, nor has it explored avenues to enhance productivity. Hence, crop simulation modeling with SSM (Simple Simulation Modeling) was used to address these issues. To palliate the lack of reliable weather data as input to the model, the validity of weather data generated by Marksim, a weather generator, was tested. Marksim provided good weather representation across a large gradient of rainfall, representative of the region, and although rainfall generated by Marksim was above observations, run-off from Marksim data was also higher, and consequently simulations using observed or Marksim weather agreed closely across this gradient of weather conditions (root mean square of error = 99 g m-2; R2 = 0.81 for pod yield). More importantly, simulation of yield changes upon agronomic or genetic alterations in the model were equally predicted with Marksim weather. A 1° × 1° grid of weather data was generated. "Drought"-related yield reduction were limited to latitudes above 12-13° North in West Central Africa (WCA) and to the Eastern fringes of Tanzania and Mozambique in East South Africa (ESA). Simulation and experimental trials also showed that doubling the sowing density of Spanish cultivars from 20 to 40 plants m-2 would increase yield dramatically in both WCA and ESA. However, increasing density would require growers to invest in more seeds and likely additional labor. If these trade-offs cannot be alleviated, genetic improvement would then need to re-focus on a plant type that is adapted to the current low sowing density, like a runner rather than a bush plant type, which currently receives most of the genetic attention. Genetic improvement targeting "drought" adaptation should also be restricted to areas where water is indeed an issue, i.e., above 12-13°N latitude in WCA and the Eastern fringes of Tanzania and Mozambique.

Determination of coefficient defining leaf area development in different genotypes, plant types and planting densities in peanut (Arachis hypogeae L.).

Rapid leaf area development may be attractive under a number of cropping conditions to enhance the vigor of crop establishment and allow rapid canopy closure for maximizing light interception and shading of weed competitors. This study was undertaken to determine (1) if parameters describing leaf area development varied among ten peanut (Arachis hypogeae L.) genotypes grown in field and pot experiments, (2) if these parameters were affected by the planting density, and (3) if these parameters varied between Spanish and Virginia genotypes. Leaf area development was described by two steps: prediction of main stem number of nodes based on phyllochron development and plant leaf area dependent based on main stem node number. There was no genetic variation in the phyllochron measured in the field. However, the phyllochron was much longer for plants grown in pots as compared to the field-grown plants. These results indicated a negative aspect of growing peanut plants in the pots used in this experiment. In contrast to phyllochron, there was no difference in the relationship between plant leaf area and main stem node number between the pot and field experiments. However, there was genetic variation in both the pot and field experiments in the exponential coefficient (PLAPOW) of the power function used to describe leaf area development from node number. This genetic variation was confirmed in another experiment with a larger number of genotypes, although possible G × E interaction for the PLAPOW was found. Sowing density did not affect the power function relating leaf area to main stem node number. There was also no difference in the power function coefficient between Spanish and Virginia genotypes. SSM (Simple Simulation model) reliably predicted leaf canopy development in groundnut. Indeed the leaf area showed a close agreement between predicted and observed values up to 60000 cm2 m-2. The slightly higher prediction in India and slightly lower prediction in Niger reflected GxE interactions. Until more understanding is obtained on the possible GxE interaction effects on the canopy development, a generic PLAPOW value of 2.71, no correction for sowing density, and a phyllochron on 53 °C could be used to model canopy development in peanut.