Changes in spatio-temporal distribution of AgMERRA-derived agro-climatic indices and agro-climatic zones for wheat crops in the northeast Iran.

This study evaluates the potential of gridded AgMERRA (the Modern-Era Retrospective Analysis for Research and Applications) to estimate aridity index (AI), growing degree days (GDD), and temperature seasonality (TS) for six land stations across northeast Iran. The researcher investigated the spatiotemporal variation of the AgMERRA-derived agro-climatic indices for the entire period 1981-2010 and three 10-year sub-periods for the 347 wheat harvested grid cells (0.25° × 0.25°) and their utility for agro-climate zoning in northeast Iran. Results indicated a good agreement between AgMERRA daily solar radiation, maximum and minimum temperatures, and annual total precipitation with corresponding land observations for the six studied sites. AgMERRA-derived evapotranspiration (ETo), AI, GDD, and TS also exhibited good agreement (R2 and d > 0.7) with the land station-derived indices for most of the locations. Annual analysis of the AI indicated a negative trend for all of the wheat harvested grid cells, but the decrease was significant (p < 0.05) only for 14.70% of grid cells, which were located in the southwest part of the studied region. The magnitude of the significant decreasing trends in annual AI was (-)0.0011 per year. The increase in aridity was due to the concurrent occurrences of positive ETo trends and negative precipitation trends. All of the wheat harvested grid cells showed a significant increasing trend (p < 0.05) for GDD at the rate of 24.10 °C d year-1. The TS series demonstrated an apparent increasing trend for 99.2% of wheat harvested grid cells; however, only 16.9% of them had the significant positive trend (p < 0.05) with the average rate of 0.023 °C year-1. The wheat harvested grid cells with increasing trend for TS were mainly distributed in the arid mountainous southern part of the study area. The 10 years sub-periods revealed that the best conditions in terms of most of the studied agro-climatic indices were found in sub-period 1981-1990 and the north Khorasan had better conditions in all three sub-periods. Based on AI, GDD, and TS, 13 major gridded agro-climatic zones were recognized in northeast Iran.

Performance of predicted evapotranspiration and yield of rainfed wheat in the northeast Iran using gridded AgMERRA weather data.

High quality of long-term daily weather data is essential for simulating crop production and its variability. However, daily weather data with adequate duration and required quality are not available in many regions. This study has evaluated the suitability of AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) weather data for simulating rainfed wheat evapotranspiration (ETc) and yield. Daily AgMERRA were compared with corresponding observed weather data of 11 land stations across the northeast Iran, considering the different periods from 1980 to 2010. Cropwat and CSM-CERES-Wheat models were used to simulate ETc and yield of rainfed wheat, respectively. The comparison of daily AgMERRA with observations resulted in the highest correlation (r2 > 70%) and good agreement (d > 0.77 and NRMSE < 30%) between climate variables, except for daily wind speed and precipitation at all locations. However, when daily precipitation data were aggregated into 15-day periods, agreement and correlation improved. According to the monthly comparison, the largest bias between AgMERRA temperature and radiation with land observations was obtained from June to August (summer season). Results also indicated that the distribution of simulated ETc and yield using AgMERRA was within 10% of the simulated yield using observations at 73% and 100% of locations, respectively. The degree of variation of AgMERRA-simulated ETc and yield was very similar to the calculated coefficient of variation in simulated ETc and yield based on observations at 73% of locations. However, simulation of ETc and yield using AgMERRA for single years was more uncertain when compared with simulated ETc and yield based on observations for the same year. It is concluded that AgMERRA can provide a robust estimate of long-term average ETc and yield of wheat than the ETc and yield of a single year in regions that there is no long-term weather data available.

Climate data clustering effects on arid and semi-arid rainfed wheat yield: a comparison of artificial intelligence and K-means approaches.

Clustering algorithms are critical data mining techniques used to analyze a wide range of data. This study compares the utility of ant colony optimization (ACO), genetic algorithm (GA), and K-means methods to cluster climatic variables affecting the yield of rainfed wheat in northeast Iran from 1984 to 2010 (27 years). These variables included sunshine hours, wind speed, relative humidity, precipitation, maximum temperature, minimum temperature, and the number of wet days. Seven climatic factors with higher correlations with detrended rainfed wheat yield were selected based on Pearson correlation coefficient significance (P value < 0.1). Three variables (i.e., sunshine hours, wind, and average relative humidity) were excluded for clustering. In the next step based on Pearson correlation (P value < 0.05) between the yield, and the seven climate attributes, fitness function, and silhouette index, only four attributes with higher correlation in its cluster were selected for reclustering. Four climate attributes had an extensive association with yield, so we used four-dimensional clustering to describe the common characteristics of low-, medium-, and high-yielding years, and this is the significance of this research that we have done four-dimensional clustering. The silhouette index showed that the best number of clusters for each station was equal to three clusters. At the last step, reclustering was done through the best-selected method. The results yielded that GA was the best method.

Disentangling the Effects of Water Stress on Carbon Acquisition, Vegetative Growth, and Fruit Quality of Peach Trees by Means of the QualiTree Model.

Climate change projections predict warmer and drier conditions. In general, moderate to severe water stress reduce plant vegetative growth and leaf photosynthesis. However, vegetative and reproductive growths show different sensitivities to water deficit. In fruit trees, water restrictions may have serious implications not only on tree growth and yield, but also on fruit quality, which might be improved. Therefore, it is of paramount importance to understand the complex interrelations among the physiological processes involved in within-tree carbon acquisition and allocation, water uptake and transpiration, organ growth, and fruit composition when affected by water stress. This can be studied using process-based models of plant functioning, which allow assessing the sensitivity of various physiological processes to water deficit and their relative impact on vegetative growth and fruit quality. In the current study, an existing fruit-tree model (QualiTree) was adapted for describing the water stress effects on peach (Prunus persica L. Batsch) vegetative growth, fruit size and composition. First, an energy balance calculation at the fruit-bearing shoot level and a water transfer formalization within the plant were integrated into the model. Next, a reduction function of vegetative growth according to tree water status was added to QualiTree. Then, the model was parameterized and calibrated for a late-maturing peach cultivar ("Elberta") under semi-arid conditions, and for three different irrigation practices. Simulated vegetative and fruit growth variability over time was consistent with observed data. Sugar concentrations in fruit flesh were well simulated. Finally, QualiTree allowed for determining the relative importance of photosynthesis and vegetative growth reduction on carbon acquisition, plant growth and fruit quality under water constrains. According to simulations, water deficit impacted vegetative growth first through a direct effect on its sink strength, and; secondly, through an indirect reducing effect on photosynthesis. Fruit composition was moderately affected by water stress. The enhancements performed in the model broadened its predictive capabilities and proved that QualiTree allows for a better understanding of the water stress effects on fruit-tree functioning and might be useful for designing innovative horticultural practices in a changing climate scenario.

Climate variability impacts on rainfed cereal yields in west and northwest Iran.

In order to assess the response of wheat and barley to climate variability, the correlation between variations of yields with local and global climate variables was investigated in west and northwest Iran over 1982-2013. The global climate variables were the El Niño-Southern Oscillation (ENSO), Arctic Oscillation (AO), and North Atlantic Oscillation (NAO) signals. Further, minimum (T min), maximum (T max), and mean (T mean) temperature, diurnal temperature range (DTR), precipitation, and reference evapotranspiration (ET0) was used as local weather factors. Pearson's correlation coefficient was applied to analyze the relationships between climatic variables and yields. Unlike T min, T mean, ET0, and T max, the yields were significantly associated with the entire growing season (EGS) DTR in most sites. Therefore, considering weather extreme variables such as DTR sheds light on the crop-temperature interactions. It is also found that the April-May-June (AMJ), October-November-December (OND), and EGS rainfall variations markedly influence the yields. Unlike the AO and NAO indices, the Niño-4 and SOI (the ENSO-related signals) were significantly correlated with the OND and EGS precipitation and DTR. Thus, the ENSO anomalies highly impact rainfed yields through influencing the OND and EGS rainfall and DTR in the studied sites. As the correlation coefficient of the OND and July-August-September (JAS) Niño-4 with yields was significant (p < 0.05) for almost all locations, the JAS and OND Niño-4 may be a good proxy for cereal yield forecasting. Further, an insignificant increment and a significant reduction in yields are expected in La Niña and El Niño years, respectively, relative to neutral years.

Vulnerability of maize production under future climate change: possible adaptation strategies.

BACKGROUND: Climate change can affect the productivity and geographic distribution of crops. Therefore, evaluation of adaptive management options is crucial in dealing with negative impacts of climate change. The objectives of this study were to simulate the impacts of climate change on maize production in the north-east of Iran. Moreover, vulnerability index which indicated that how much of the crop yield loss is related to the drought was computed for each location to identify where adaptation and mitigation strategies are effective. Different sowing dates were also applied as an adaptation approach to decrease the negative impacts of climate change in study area. RESULTS: The results showed that the maize yield would decline during the 21st century from -2.6% to -82% at all study locations in comparison with the baseline. The result of vulnerability index also indicated that using the adaptation strategies could be effective in all of the study areas. Using different sowing dates as an adaptation approach showed that delaying the sowing date will be advantageous in order to obtain higher yield in all study locations in future. CONCLUSION: This study provided insight regarding the climate change impacts on maize production and the efficacy of adaptation strategies. © 2016 Society of Chemical Industry.

Peach water relations, gas exchange, growth and shoot mortality under water deficit in semi-arid weather conditions.

In this study the sensitivity of peach tree (Prunus persica L.) to three water stress levels from mid-pit hardening until harvest was assessed. Seasonal patterns of shoot and fruit growth, gas exchange (leaf photosynthesis, stomatal conductance and transpiration) as well as carbon (C) storage/mobilization were evaluated in relation to plant water status. A simple C balance model was also developed to investigate sink-source relationship in relation to plant water status at the tree level. The C source was estimated through the leaf area dynamics and leaf photosynthesis rate along the season. The C sink was estimated for maintenance respiration and growth of shoots and fruits. Water stress significantly reduced gas exchange, and fruit, and shoot growth, but increased fruit dry matter concentration. Growth was more affected by water deficit than photosynthesis, and shoot growth was more sensitive to water deficit than fruit growth. Reduction of shoot growth was associated with a decrease of shoot elongation, emergence, and high shoot mortality. Water scarcity affected tree C assimilation due to two interacting factors: (i) reduction in leaf photosynthesis (-23% and -50% under moderate (MS) and severe (SS) water stress compared to low (LS) stress during growth season) and (ii) reduction in total leaf area (-57% and -79% under MS and SS compared to LS at harvest). Our field data analysis suggested a Ψstem threshold of -1.5 MPa below which daily net C gain became negative, i.e. C assimilation became lower than C needed for respiration and growth. Negative C balance under MS and SS associated with decline of trunk carbohydrate reserves--may have led to drought-induced vegetative mortality.

Water scarcity conditions affect peach fruit size and polyphenol contents more severely than other fruit quality traits.

BACKGROUND: The literature abounds with the impacts of drought conditions on the concentration of non-structural compounds (NSC) in peach fruits without distinction as to the direct effect of drought on fruit metabolism and its indirect effect through dilution. Moreover, there is a need to investigate the sensitivity of the fruit composition to progressive water deficit in semi-arid conditions, as well as the origin of variations in fruit composition - not only in carbohydrates and organic acids, but also in secondary metabolites such as polyphenols. RESULTS: The increase in stress intensity resulted in smaller fruits and a reduction in yield. Drought increased fruit dry matter content, structural dry matter (SDM) content and firmness due to lower water import to fruits, although drought reduced fruit surface conductance and its transpiration. Drought significantly affected the concentrations of each NSC either through the decrease in dilution and/or modifications of their metabolism. The increase in hexoses and sorbitol concentrations of fruits grown under drought conditions resulted in an increase in the sweetness index but not near harvest. Malic acid concentration and content:SDM ratio increased as drought intensified, whereas those of citric and quinic acids decreased. Polyphenol concentration and content increased under severe drought. CONCLUSION: The increase in stress intensity strongly affected fruit mass. The concentration of total carbohydrates and organic acid at harvest increased mainly through a decrease in fruit dilution, whereas the concentrations of polyphenols were also strongly affected through an impact on their metabolism.

Effects of precipitation and temperature on crop production variability in northeast Iran.

Climate variability adversely impacts crop production and imposes a major constraint on farming planning, mostly under rainfed conditions, across the world. Considering the recent advances in climate science, many studies are trying to provide a reliable basis for climate, and subsequently agricultural production, forecasts. The El Niño-Southern Oscillation phenomenon (ENSO) is one of the principle sources of interannual climatic variability. In Iran, primarily in the northeast, rainfed cereal yield shows a high annual variability. This study investigated the role played by precipitation, temperature and three climate indices [Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and NINO 3.4] in historically observed rainfed crop yields (1983-2005) of both barley and wheat in the northeast of Iran. The results revealed differences in the association between crop yield and climatic factors at different locations. The south of the study area is a very hot location, and the maximum temperature proved to be the limiting and determining factor for crop yields; temperature variability resulted in crop yield variability. For the north of the study area, NINO 3.4 exhibited a clear association trend with crop yields. In central locations, NAO provided a solid basis for the relationship between crop yields and climate factors.

Gene-based modelling for rice: an opportunity to enhance the simulation of rice growth and development?

Process-based crop simulation models require employment of new knowledge for continuous improvement. To simulate growth and development of different genotypes of a given crop, most models use empirical relationships or parameters defined as genetic coefficients to represent the various cultivar characteristics. Such a loose introduction of different cultivar characteristics can result in bias within a simulation, which could potentially integrate to a high simulation error at the end of the growing season when final yield at maturity is predicted. Recent advances in genetics and biomolecular analysis provide important opportunities for incorporating genetic information into process-based models to improve the accuracy of the simulation of growth and development and ultimately the final yield. This improvement is especially important for complex applications of models. For instance, the effect of the climate change on the crop growth processes in the context of natural climatic and soil variability and a large range of crop management options (e.g., N management) make it difficult to predict the potential impact of the climate change on the crop production. Quantification of the interaction of the environmental variables with the management factors requires fine tuning of the crop models to consider differences among different genotypes. In this paper we present this concept by reviewing the available knowledge of major genes and quantitative trait loci (QTLs) for important traits of rice for improvement of rice growth modelling and further requirements. It is our aim to review the assumption of the adequacy of the available knowledge of rice genes and QTL information to be introduced into the models. Although the rice genome sequence has been completed, the development of gene-based rice models still requires additional information than is currently unavailable. We conclude that a multidiscipline research project would be able to introduce this concept for practical applications.