Agricultural Meteorology
S. Shiukhy Soqanloo; M. Mousavi Baygi; B. Torabi; M. Raeini Sarjaz
Abstract
IntroductionWheat (Triticum aestivum L.) has become very important as a valuable strategic product with high energy level. The importance of investigating environmental stresses and their role in predicting and evaluating the growth and crops yield is essential. A wide range of plant response to stress ...
Read More
IntroductionWheat (Triticum aestivum L.) has become very important as a valuable strategic product with high energy level. The importance of investigating environmental stresses and their role in predicting and evaluating the growth and crops yield is essential. A wide range of plant response to stress is extended to morphological, physiological and biochemical responses. Considering the rapid advancement in computer model development, plant growth models have emerged as a valuable tool to predict changes in production yield. These growth simulation models effectively incorporate the intricate influences of various factors, such as climate, soil characteristics, and management practices on crop yield. By doing so, they offer a cost-effective and time-efficient alternative to traditional field research methods. Material and MethodsThis research was conducted in the research farm of Varamin province, which has a silty loam soil texture. The latitude and longitude of the region are 35º 32ʹ N and 51º 64ʹ E, respectively. Its height above sea level is 21 meters. According to Demarten classification, Varamin has a temperate humid climate. The long-term mean temperature of Varamin is 11.18 ° C and the total long-term rainfall is 780 mm. In this study, in order to simulate irrigated wheat cv. Mehregan growth under drought stress, an experimental based on completely randomized blocks (CRBD) including: non-stress as control (NS), water stress at booting stage (WSB), water stress at flowering stage (WSF), water stress at milking stage (WSM) and water stress at doughing stage (WSD) with three replications during growth season 2019-2020 was carried out in Varamin, Iran. Crop growth simulation was done using SSM-wheat model. This model simulates growth and yield on a daily basis as a function of weather conditions, soil characteristics and crop management (cultivar, planting date, plant density, irrigation regime). Results and DiscussionBased on the results, the simulation of the phenological stages of irrigated wheat cv. Mehregan under water stress condition using SSM-wheat model showed that there was no difference between observed and simulated values. Summary, the values of day to termination of seed growth (TSG) were observed under non- stress, stress in the booting stage, flowering, milking and doughing of the grains, 222, 219, 219, 221, 221 days, respectively andsimulation values with 224, 221, 220, 221, respectively. However, with their simulation values, there were slight differences with 224, 221, 220, 221, respectively. Acceptable values of RMSE (11.7 g.m-2) and CV (3.5) indexes showed the high ability of the SSM model in simulating the grain yield of irrigated wheat cv. Mehregan under water stress conditions. Grain yield values were observed in non-stress conditions of 5783, water stress in booting, flowering, milking and doughing of the grain stages in 5423, 5160, 5006 and 5100 kg. h-1, respectively. While the simulated values were 5630, 5220, 4920, 4680 and 4880 kg. h-1, respectively. Based on the findings, observed and simulated values of leaf area index (LAI) were observed under water stress condition in the booting, flowering, milking and doughing of the grain stages (4.3 and 4.47), (4.33) and 4.46), (4.4 and 4.57) and (4.4 and 4.58) cm-2, respectively. Evaluation of the 1000-grain weight of irrigated wheat cv. Mehregan under the water stress showed that the SSM model was highly accurate. RMSE (4.6 g.m-2) and CV (1.8) values indicate the ability of the SSM model to simulate the 1000-grain weight of irrigated wheat cv. Mehregan. Also, the simulated values of the harvest index were 34.7 % in non-stress conditions, which decreased by 6 % compared to the observed value. Harvest index values were observed under water stress conditions in the in the booting, flowering, milking and doughing of the grain stages in 30.2, 29.3, 29.9 and 29.5 %, respectively. Compared to its observed values, it was reduced by 3, 3.5, 5, and 5.5 %, respectively. ConclusionBased on the findings, the slight difference between the observed and simulated values demonstrates the SSM model's capability to accurately capture water stress impacts on the phenological stages, grain yield, and yield components of irrigated wheat cv. Mehregan during critical growth stages, including booting, flowering, milking, and doughing. The results indicate that the SSM model is effective in simulating wheat growth under water stress conditions, showcasing its potential as a valuable tool for modeling irrigated wheat growth. The model's ability to account for water stress and its effects on various growth parameters makes it a reliable and efficient tool for predicting crop performance in water-limited environments.
Irrigation
H. Ramezani Etedali; F. Safari
Abstract
IntroductionEvaluation of plant models in agriculture has been done by many researchers. The purpose of this work is to determine the appropriate plant model for planning and predicting the response of crops in different regions. This action is made it possible to study the effect of various factors ...
Read More
IntroductionEvaluation of plant models in agriculture has been done by many researchers. The purpose of this work is to determine the appropriate plant model for planning and predicting the response of crops in different regions. This action is made it possible to study the effect of various factors on the performance and efficiency of plant water consumption by spending less time and money. Since the most important agricultural product in Iran is wheat, so proper management of wheat fields has an important role in food security and sustainable agriculture in the country. The main source of food for the people in Iran is wheat and its products, and any action to increase the yield of wheat is necessary due to limited water and soil resources. Evapotranspiration is a complex and non-linear process and depends on various climatic factors such as temperature, humidity, wind speed, radiation, type and stage of plant growth. Therefore, in the present study, by using daily meteorological data of Urmia, Rasht, Qazvin, Mashhad and Yazd stations, the average daily evapotranspiration values based on the results of the FAO-Penman-Monteith method are modeled and the accuracy of the two methods temperature method (Hargreaves-Samani and Blaney-Criddle) and three radiation methods (Priestley-Taylor, Turc and Makkink) were compared with FAO-56 for wheat.Materials and MethodsThe present study was conducted to evaluate the accuracy and efficiency of the AquaCrop model in simulation of evapotranspiration and biomass, using different methods for estimation reference evapotranspiration in five stations (Urmia, Qazvin, Rasht, Yazd and Mashhad). Four different climates (arid, semi-arid, humid and semi-humid) were considered in Iran for wheat production. The equations used to estimate the reference evapotranspiration in this study are: Hargreaves-Samani (H.S), Blaney-Criddle (B.C), Priestley-Taylor (P.T), Turc (T) and Makkink (Mak). Then, the results were compared with the data of the mentioned stations for wheat by error statistical criteria including: explanation coefficient (R2), normal root mean square error (NRMSE) and Nash-Sutcliffe index (N.S).Results and DiscussionThe value of the explanation coefficient (R2) of simulation ET and biomass in the Blaney-Criddle method is close to one, which shows a good correlation between the data. The NRMSE and Nash-Sutcliffe values for both parameters and the five stations are in the range of 0-20 and close to one, respectively, which indicates the AquaCrop model's ability to simulate ET and biomass. On the other hand, the value of R2 in the Hargreaves-Samani method for biomass close to one, NRMSE in the range of 0-10 and Nash-Sutcliffe index is more than 0.5, which indicates a good simulation. The NRMSE index in the evaluation of ET and biomass wheat is excellent for the Blaney-Criddle method and about Hargreaves-Samani for ET is poor and for the biomass is excellent.The Turc method with NRMSE in the range of 0-30, explanation coefficient close to or equal to one and a Nash-Sutcliffe index of one or close to one can be used to simulate ET and biomass at all five stations. Also, for biomass simulation, Priestley-Taylor and Makkink methods have acceptable statistical values in all five stations.Based on the value of explanation coefficient (R2) of estimation ET and biomass wheat for radiation methods, the correlation between the data in all three radiation methods is high. Percentage of NRMSE index of Makkink method for wheat in ET evaluation in Qazvin station is poor category and in Urmia and Rasht is good and in Mashhad and Yazd is moderate and about biomass in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd) is excellent category, the error percentage of Priestley-Taylor method for wheat in ET evaluation in Yazd station is good and the rest of the stations is poor, about biomass is excellent in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd). The error rate of Turc method for wheat in ET evaluation in Urmia, Rasht and Mashhad stations is good and in Qazvin and Yazd is poor and about biomass is excellent in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd).ConclusionAccording to the results obtained using Blaney-Criddle method with R2 value close to one, NRMSE in the range of 0-20% (excellent to good) and Nash-Sutcliffe index close to one and Turc method with R2 value close to one, NRMSE in the range of 0-10% (excellent) and Nash-Sutcliffe index close to one was showed a good accuracy of AquaCrop model in simulation of evapotranspiration and biomass with these methods of estimation of evapotranspiration compared to other methods.
Farshid Ramezani; Abbass Kaviani; Hadi Ramezani Etedali
Abstract
Introduction: AquaCrop model was developed to simulate crop response to water consumption and irrigation management. The model is easy to use, works with limited input, and has acceptable accuracy. In this study, the data of an alfalfa field (as a perennial fodder plant) in the Iranian city of Ardestan ...
Read More
Introduction: AquaCrop model was developed to simulate crop response to water consumption and irrigation management. The model is easy to use, works with limited input, and has acceptable accuracy. In this study, the data of an alfalfa field (as a perennial fodder plant) in the Iranian city of Ardestan was used to calibarate and validate the performance of AquaCrop model to simulate the crop productivity in relation to water supply and irrigation management.
Materials and Methods: The data of Fajr-e Esfahan Company farms of Ardestan County were used for calibration and validation of the AquaCrop model, simulating the alfalfa performance in different harvests and over different years. The farms are 1004 m above sea level and located in 33°2' to 33°30' North and 55°20' to 55°22' East. The farm under investigation included ten plots of alfalfa field, with an area of 280 hectares. The data of two plots were used for calibration and, two others used for validation.
Considering that alfalfa is a perennial plant, the data regarding the first harvest was defined as sowing, and transplanting was used to refer to the next harvests. Considering the physiological changes of plants over a year and during different harvests, the numerical value of different parameters, including primary vegetation, maximum vegetation, the depth of primary root development, the maximum depth of primary root development, crop coefficient, germination date, flowering, vegetation senescence, and physiological maturity, were defined for the model. The CRM, NRMSE, R2, and EF indices were used for verification of the calibration results. The CRM index determines the overestimation or underestimation of the model. The EF index is variable between 1 and 0, where 1 indicates optimal performance of the model. If all estimated and measured values were equal, the value of CRM and NRMSE would be zero, and EF would be one.
Results and Discussion:After calibration, validation was performed to examine the performance of the model. Hence, the actual performance rate for different harvests and the results of simulations were compared. Lower NRMSE value is indicative of high accuracy of the model in estimation of the performance. The value of CRM was mostly positive, showing the underestimation of the model in most of the simulations. The maximum performance happened during the first harvest year. The annual harvest decreased with an average rate of 1.2, compared to former years. The evaporation and transpiration rate was calculated by the model and the results were compared with potential evapotranspiration (FAO Penman-Monteith) and National Document of Irrigation (NET WAT). The reference crop evapotranspiration (ET0) had the highest value, and was calculated through FAO Penman-Monteith equation. The numerical value of potential crop evapotranspiration (ETc), which is the result of multiplication of crop coefficient by reference crop evapotranspiration (ET0), was greater than the results of the model, i.e. the estimated actual evapotranspiration. The discrepancy between them is the result of stress coefficient (ET0×Kc×Ks), which the model takes into account in estimation of actual plant water requirement. Evapotranspiration refers to two factors, namely the water lost by transpiration from plants and by evaporation from the soil. The plant transpiration and green cover are considered to be the generating part; AquaCrop is able to examine and improve transpiration efficiency through managerial statements. The values of transpiration from plants and evaporation from the soil for alfalfa were differentiated from the values estimated by the model. The productivity of evaporation, transpiration, and evapotranspiration were calculated by the model. The difference in the productivity values of the plots during different years was the result of difference in chemical composition, harvest index, and transpiration rate.
Conclusion:The AquaCrop model performed well in simulation of crop performance compared to actual annual, and even monthly, performance, and its results were very close to the actual performance. The model is sensitive to temperature changes, and it is suggested to use the Growing Degree Days (GDD) instead of Calendar Days section. . The Version 5 of AquaCrop model can, in addition to moisture stress, include salinity stress in calculations; this is evident in the variation of actual evaporation and transpiration values estimated by the model. In this study, the annual evaporation and transpiration rate was predicted by the model. The higher rate of evaporation can lead to a 27 to 44 percent decrease in the efficiency of evapotranspiration (Y ET-1), compared to transpiration efficiency (Y T-1).
fatemeh yaghoubi; Mohammad Bannayan Aval; Ghorban Ali Asadi
Abstract
Introduction: Estimating crop water requirement, crop yield and their temporal and spatial variability using crop simulation models are essential for analysis of food security, assessing impact of current and future climates on crop yield and yield gap analysis, however it requires long-term historical ...
Read More
Introduction: Estimating crop water requirement, crop yield and their temporal and spatial variability using crop simulation models are essential for analysis of food security, assessing impact of current and future climates on crop yield and yield gap analysis, however it requires long-term historical daily weather data to obtain robust predictions. Depending on the degree of weather variability among years, at least 10–20 years of daily weather data are necessary for reliable estimates of crop yield and its inter-annual variability. In many regions where crops are grown, daily weather data of sufficient quality and duration are not available. In this way, gridded weather databases with complete terrestrial coverage are available which require comprehensive validation before any application. These weather databases typically derived from global circulation computer models, interpolated weather station data or remotely sensed surface data from satellites. The aims of this study were to evaluate differences between grided AgMERRA weather data and ground observed data and quantify the impact of such differences on simulated water requirement and yield of rainfed wheat at 9 different locations in Khorasan Razavi province.
Materials and Methods: AgMERRA dataset (NASA’s Modern-Era Retrospective analysis for Research and Applications) was selected as the girded weather data source for use in this study because it is publically accessible. We evaluated AgMERRA weather data against observed weather data (OWD) from 9 meteorological stations (Torbat Jam, Torbat Heydarieh, Sabzevar, Sarakhs, Ghoochan, Kashmar, Gonabad, Mashhad, and Neyshabour) in Khorasan Razavi province. For each weather variable (solar radiation, maximum temperature, minimum temperature, precipitation, and wind speed), the degree of correlation and agreement between OWD and AgMERRA data for the grid cell in which weather stations were located were evaluated. The intercept (b), slope (m), and coefficient of determination (r2) of the linear regression were calculated to determine the strength and bias of the relationship, while the root mean square error (RMSE) and normalized root mean square error (NRMSE) were computed to measure the degree of agreement between data sources. Crop water requirement or actual crop evapotranspiration (ETc) under standard condition was computed using CROPWAT 8.0. The CSM-CERES-Wheat (Cropping System Model-Crop Environment Resource Synthesis-Wheat) model, included in the Decision Support System for Agrotechnology Transfer (DSSAT v4.6) software package was used to calculate rainfed wheat yield. For each location in this study, rainfed wheat grain yield and water requirement were simulated using ground-observed and AgMERRA weather data and outputs were compared with each other.
Results and Discussion: The results of this study showed that AgMERRA daily maximum and minimum temperature and solar radiation showed strong correlation and good agreement with data from ground weather stations. AgMERRA daily precipitation had low correlation and good agreement (mean r2= 0.34, RMSE= 2.25 mm and NRMSE= 4.94% across the 9 locations) with OWD daily values, but correlation with 15-day precipitation totals were much better (mean r2 >0.7 across the 9 locations). There was reasonable agreement between a number of observed dry and wet days with AgMERRA compared to OWD. Results indicated that coefficient of variation of simulated water requirement and yield using AgMERRA weather data was remarkably similar to the degree of variation observed in simulated water requirement and yield using OWD at all locations (distribution of CVs in simulated water requirement and yield using AgMERRA weather data were within ±5% of the CV calculated for simulated water requirement and yield using observed weather data) except Torbat Jam, Torbat Heydarieh and Gonabad for water requirement and Mashhad, Kashmar and Ghoochan for yield. There was good agreement between long-term average yield simulated with AgMERRA weather data and long-term average yield simulated using observed weather data. For example, the distribution of simulated yields using AgMERRA data was within 10% of the simulated yields using observed data at all locations. Using AgMERRA weather data resulted in simulated crop water requirement that were not in close agreement with crop water requirement simulated with ground station data at two location including Gonabad and Torbat Heydarieh.
Conclusions: These results supported the use of uncorrected AgMERRA daily maximum and minimum temperature and solar radiation in areas that their weather stations only have a few years of daily weather records available or areas without weather station. Considering the advantage of continuous coverage and availability, use of AgMERRA dataset appears to be a promising option for simulation of long-term average yield and water requirement, as well as for assessing impact of climate change on crop production and also estimating the magnitude of existing gaps between yield potential and current average farm yield in Khorasan Razavi province. But they are not very reliable for accurate simulation of water requirement and yield in a specific year and estimate their inter-annual variation.
M. Delghandi; S. Broomandnasab; B. Andarzian; A.R. Massah-Bovani
Abstract
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 ...
Read More
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.
