The design and evaluation of travelling gun irrigation systems: enrolador software

Technical Paperinfo:eu-repo/semantics/publishedVersio

Species Diversity and Identification of Plant Functional Types of Woodland in Shimbar Protected Area, Khuzestan Provience

Measuring the diversity of plant functional types, identifying their characteristics, and their classification will help to identification of woodland germination capacity and implementing appropriate range management programs. The study was designed to measure the species diversity and to identify plant functional types in three adjacent ecological sites in Shimbar or Shirin Bahar region. During winter, spring and summer since 2013 to 2014, the data regarding the percentage of species coverage were taken from 106 plots using stratified random sampling method in the south facing slopes, north facing slopes and the wetland. Species diversity (Alpha diversity) and habitat diversity (Beta diversity) were measured using PAST and SDR softwares. According to the Shannon-Wiener and Simpson indices the greatest species diversity were found in the wetland, south slopes and north slopes respectively. Species richness was higher in northern slope than northern slopes and the lowest in wetland. For classifying and determining response of vegetation to environmental factors and identifying plant functional types, about 66 resistance to disturbance characters were measured and subjected to clustering by Ward method in R software. The annual and perennial species were classified into 21 and trees and shrubs to 8 classes

Adaptation Strategies of Wheat to Climate Change (Case Study: Ahvaz Region)

Introduction In recent years human activities induced increases in atmospheric carbon dioxide (CO2). Increases in [CO2] caused global warming and Climate change. Climate change is anticipated to cause negative and adverse impacts on agricultural systems throughout the world. Higher temperatures are expected to lead to a host of problems. On the other hand, increasing of [CO2] anticipated causing positive impacts on crop yield. Considering the socio-economic importance of agriculture for food security, it is essential to undertake assessments of how future climate change could affect crop yields, so as to provide necessary information to implement appropriate adaptation strategies. In this perspective, the aim of this study was to assess potential climate change impacts and on production for one of the most important varieties of wheat (chamran) in Khouzestan plain and provide directions for possible adaptation strategies. Materials and Methods: For this study, The Ahvaz region located in the Khuzestan province of Iran was selected. Ahvaz has a desert climate with long, very hot summers and mild, short winters. At first, thirteen GCM models and two greenhouse gases emission (GHG) scenarios (A2 and B1) was selected for determination of climate change scenarios. ∆P and ∆T parameters at monthly scale were calculated for each GCM model under each GHG emissions scenario by following equation: Where ∆P, ∆T are long term (thirty years) precipitation and temperature differences between baseline and future period, respectively. average future GCM temperature (2015-2044) for each month, , average baseline period GCM temperature (1971-2000) for each month, , average future GCM precipitation for each month, , average baseline period GCM temperature (1971-2000) for each month and i is index of month. Using calculated ∆Ps for each month via AOGCM models and Beta distribution, Cumulative probability distribution function (CDF) determined for generated ∆Ps. ∆P was derived for risk level 0.10 from CDF. Using the measured precipitation for the 30 years baseline period (1971-2000) and LARS-WG model, daily precipitation time series under risk level 0.10 were generated for future periods (2015-2045 and 2070-2100). Mentioned process in above was performed for temperature. Afterwards, wheat growth was simulated during future and baseline periods using DSSAT, CERES-Wheat model. DSSAT, CERES4.5 is a model based on the crop growth module in which crop growth and development are controlled by phenological development processes. The DSSAT model contains the soil water, soil dynamic, soil temperature, soil nitrogen and carbon, individual plant growth module and crop management module (including planting, harvesting, irrigation, fertilizer and residue modules). This model is not only used to simulate the crop yield, but also to explore the effects of climate change on agricultural productivity and irrigated water. For model validation, field data from different years of observations were used in this study. Experimental data for the simulation were collected at the experimental farm of the Khuzestan Agriculture and Natural Resources Research Center (KANRC), located at Ahwaz in south western Iran. Results and Discussion: Results showed that wheat growth season was shortened under climate change, especially during 2070-2100 periods. Daily evapotranspiration increased and cumulative evapotranspiration decreased due to increasing daily temperatures and shortening of growth season, respectively. Comparing the wheat yield under climate change with base period based on the considered risk value (0.10) showed that wheat yield in 2015-2045 and 2070-2100 was decreased about 4 and 15 percent, respectively. Four adaptation strategies were assessed (shifting in the planting date, changing the amount of nitrogenous fertilizer, irrigation regime and breeding strategies) in response to climate change. Results indicated that Nov, 21 and Dec, 11 are the best planting dates for 2015-2045 and 2070-2100, respectively. The late season varieties with heat-tolerant characteristic had higher yield in comparison with short and normal season varieties. It indicated that breeding strategy was an appropriate adaptation under climate change. It was also found that the amount of nitrogen application will be reduced by 20 percent in future periods. The increase and decease of one irrigation application (40mm) to irrigation regime of base period resulted in maximum yield for 2015-2045 and 2070-2100, respectively. But, reduction of two irrigation application (80mm) resulted in maximum water productivity (WPI). Conclusions In the present study, four adaptation strategies of wheat (shifting in the planting date, changing the amount of nitrogenous fertilizer, irrigation regime and breeding strategies) under climate change in Ahvaz region were investigated. Result showed that Nov, 21 and Dec, 11 were the best planting dates for 2015-2045 and 2070-2100, respectively. The late season varieties with heat-tolerant characteristic had higher yield in comparison with short and normal season varieties. It indicated that breeding strategy was an appropriate adaptation strategy under climate change. It was also found that the amount of nitrogen application will be reduced by 20 percent in future periods. The increase and decease of one irrigation application (40mm) to irrigation regime of base period resulted in maximum yield for 2015-2045 and 2070-2100, respectively

Modelling the Grain Yield of Wheat in Irrigated Saline Environment with Foliar Potassium Fertilization

In the present study, an effort was made to calibrate and validate a crop model using the experiment generated data pertaining to growth and yield of wheat cultivars under irrigated saline environment using foliar potassium application. A field experiment was conducted at Water Technology Centre research farm, Indian Agricultural Research Institute, New Delhi, in split–split plot design during rabi 2011–2012 and 2012–2013 to generate data for simulation of FAO AquaCrop model. Two wheat cultivars (i.e. one salt tolerant KRL-1-4 and other salt susceptible HD 2894) under four salinity levels, viz, GW (1.7 dS m−1), 4, 8 and 12 dS m−1 were experimented under foliar and non-foliar treatments. AquaCrop model was calibrated using experiment data of rabi 2011–2012 and validated with data of rabi 2012–2013. The model evaluation parameters, viz, model efficiency (ME), index of agreement (d) and R 2 pertaining to grain yield of both wheat cultivars predicted by validated model were averaged to be 0.86, 0.95 and 0.96, respectively, under all treatment levels. Moreover, the simulated biomass yield of both the cultivars was with average values of ME, d and R 2 as 0.91, 0.97, and 0.93, respectively. However, model validation results for water productivity were 0.60, 0.82 and 0.93 for ME, d and R 2, respectively. It was observed that the AquaCrop model simulation for grain yield was better as compared to biomass and water productivity for all treatment levels. Validated model resulted in predicted grain yield of both wheat cultivars with low prediction error (i.e. 1.77–6.52%) up to 8 dS m−1, whereas at 12 dS m−1 salinity level, the prediction error increased and varied from 21.7 to 37.5%. Nonetheless, the FAO AquaCrop model can be used with acceptable accuracy for simulation of grain yield of wheat cultivars using saline irrigation up to 8 dS m−1 with foliar potassium fertilization