Plant Productions

Evaluation of withholding irrigation on different canola cultivars under autumn cultivation conditions: a simulation study

Mozhgan Sadeghi; Sajjad Rahimi-Moghaddam; Esmaeil Mohammadi-Ahmadmahmoudi; Khosro Azizi

Articles in Press, Accepted Manuscript, Available Online from 01 January 2025

https://doi.org/10.22055/ppd.2025.47907.2203

Abstract

Introduction: Canola is a crucial industrial crops in the country's oil production in order to produce high-quality edible oil. However, the challenges related to water allocation to ... Read More Introduction: Canola is a crucial industrial crops in the country's oil production in order to produce high-quality edible oil. However, the challenges related to water allocation to the spring crops at the end of the autumn canola growth period have become one of the obstacles to achieving optimal production levels for this crop. Addressing this challenge is essential for enhancing the productivity and sustainability of rapeseed farming in the country.Materials and Methods: This research aimed to evaluation the effect of withholding irrigation treatments on different growth stages of autumn canola production. It also seeks to identify the suitable canola cultivar and the optimal growth stage for implementing withholding irrigation through a long-term simulation experiment using the APSIM-canola simulation model. It was carried out in four counties of Lorestan province including Khorramabad, Pol-e Dokhtar, Kuhdasht and Aleshtar in Faculty of Agriculture, Lorestan University. For this purpose, in the first step, the model was evaluated using two independent data sets. The cultivars included Hyola308, Hyola401 and RGS003 (early, mid and late maturity cultivars, respectively), and the irrigation treatments of the study included full irrigation, withholding irrigation at the flowering stage, withholding irrigation at the pod initiation stage, and withholding irrigation at the grain filling stage.Results and Discussion: The evaluation of the APSIM-canola simulation model demonstrated that this model has a good capability to accurately predict autumn canola grain yield evidenced by an estimation error (nRMSE) of 18%. The results showed that the variability of the autumn canola grain yield across all of the studied locations, different treatments of withholding irrigation and cultivars was significant. Notably, the highest yield was recorded at 4.49 t ha-1 in Khorramabad, while the lowest was 1.96 t ha-1 in Aleshtar. Furthermore, the analysis of dry matter production indicated a considerable variability across different locations and treatments in a way that the highest and lowest amount of dry matter between location belonged to Khorramabad and Aleshtar (17.80 and 8.89 t ha-1, respectively). The research findings highlighted the crucial role of climatic conditions, particularly air temperature, in informing management practices such as cultivar selection and irrigation strategies. Based on this, the use of Hyola401 mid maturity cultivar in Pol-e Dokhtar, Khorramabad and Kuhdasht counties along with full irrigation throughout the growing season resulted in the highest grain yields of 5.38, 5.36, and 4.49 t ha-1, respectively. It was while in Aleshtar (with cooler temperature condition compared to other locations) employing the early maturity cultivar of Hyola308 coupled with withholding of irrigation before the end of the growing season led to the highest amount of grain yield (2.64 t ha-1) in this region.Conclusion: The findings of this research indicate that in the cultivation of autumn canola, it is crucial to consider climatic factors, particularly air temperature, as well as the accessibility of water resources when selecting the appropriate cultivar. Specifically, under conditions where water availability is restricted, opting for early-maturing and mid-maturing cultivars can serve as an effective strategy. This approach not only mitigates the adverse impacts of water scarcity but also contributes to the sustainable production of canola in various regions. By aligning cultivar selection with the specific climatic and water conditions of an area, farmers can enhance crop resilience and ensure a more stable yield, ultimately supporting the agricultural sustainability of their communities.

Simulating response of lentil cultivars to sowing date and initial soil moisture in Lorestan province

Mojtaba Koohro; Sajjad Rahimi-Moghaddam; khosro Azizi; Saeid Heidari; Seyed Reza Amiri

Volume 47, Issue 3 , November 2024, , Pages 475-489

https://doi.org/10.22055/ppd.2024.45041.2127

Abstract

Introduction Legumes are the second food source after cereals, which provide almost a quarter of protein for developing countries. Lentil is recognized as one of important legumes ... Read More Introduction Legumes are the second food source after cereals, which provide almost a quarter of protein for developing countries. Lentil is recognized as one of important legumes due to its favourable characteristics. The lintile production, like to other crops has been influenced by three factors: management, genetics, and environment. Improving crop production can be achieved by optimizing these factors. Thus, paying attention to the above-mentioned factors can reduce the severe climatic effects on crop production. Among the above-mentioned factors, management strategies e.g., optimal sowing date and using the optimal cultivar are considered to improve crop production. Initial soil moisture, which can affect crop germination, establishment and ultimately growth and yield, is another important strategy. Accordingly, the current research was conducted in order to simulate the effects of cultivar, sowing date, and initial soil moisture on lentil grian yield in different locations of Lorestan province. Materials and Methods The study locations were Aligudarz, Nurabad, KhorramAbad, and Kuhdasht. Simple Simulation Models-iCrop2 (SSM-iCrop2) was used to simulate the lentil growth and development. The data required to run model included climatic, soil, management, and crop data. Daily long-term climatic data including maximum and minimum temperature, rainfall, and radiation were collected from Iran Meteorological Organization. The soil data included soil depth, soil water content at wilting point, soil water content at field capacity, and saturation water content, which were obtained from different data collection in the Ministry of Agriculture and Agricultural, the Natural Resources Research and Education Centers, and soil laboratories at each location and Food and Agriculture Organization and Global yield Gap Atlas.The management data such as palnt density, tillage, rows distance, and sowing depth were obtained by local experts from the Ministry of Agriculture and Agricultural and the Natural Resources Research and Education Centers at each location. The crop data e.g., the specific genetic coefficients of each cultivar were obtained form Amiri and Deihimfard (2018). The study treatments consisted of four sowing dates (21 January, 12 February, 4 March, and 30 April), two cultivars (early-maturity and late-maturity) and four initial soil moisture (32, 36, 42, and 58 mm). The model was run for 41 years (1980-2020). Initial soil water contents and sowing dates were obtained from a preliminary simulation experiment.Results and DiscussionAveraged across all treatments, the highest grain yield was obtained at KhorramAbad (388 kg ha-1) due to higher rainfall during the growing season. In addition, the grain yield difference among the sowing dates ranged from kg ha-1 on 30 April to 364 kg ha-1 on 21 January. The reason for the higher grain yield on 21 January sowing date was the higher cumulative rainfall season (149.3 mm vs. 99.2 mm) and the lower mean temperature (22.9 °C vs. 23.9 °C) during the growing season compared to other sowing dates. Across locations, sowing dates, and cultivars, the highest grain yield (263 kg ha-1) was simulated under initial soil moisture of 58 mm. Also, on average across sowing dates, initial soil moisture contents, and locations, the early-maturity cultivar simulated more grain yield than late-maturity cultivar (405 kg ha-1 vs. 63 kg ha-1) due to the avoidance of drought stress at the end of the growing season. Considering different interactions, on average across locations, the highest grain yield was simulated by 704 kg ha-1 in the combination of early-maturity cultivar, early sowing date (21 January), and initial soil moisture of 58 mm. Conclusion In general, the results indicated that the lentil grain yield varied among the various study treatments (sowing date, cultivar, and initial soil moisture) as well as different locations of Lorestan province. Lentil growers can increase the lentil production in Lorestan province and study locations by using the interaction of early planting date (21 January) ´ early-maturity cultivar (Kimia) ´ higher initial soil moisture (58 mm). It should be noted that the current research was conducted under water-limited condition and it is suggested that researchers focus on other limitimg and reducing factors in the future studies.

Evaluating APSIM-Barley model for different barley cultivars under water and nitrogen stresses

Hassan Shahbazi; khosro Azizi; Sajjad Rahimi-Moghaddam; Solieman Mohamadi; Naser Akbari

Volume 47, Issue 1 , June 2024, , Pages 1-17

https://doi.org/10.22055/ppd.2023.43267.2092

Abstract

Introduction Background: Crop simulation models provide the possibility to study the effect of agronomic management practices on agricultural production activities in a given location. ... Read More Introduction Background: Crop simulation models provide the possibility to study the effect of agronomic management practices on agricultural production activities in a given location. The models are mechanistic and process-oriented that are able to simulate different agricultural systems (including arid and semi-arid areas such as Iran) through different sub-models (biological, environmental, managerial, and economic sub-models) connected with the main engine of the models. The APSIM-Barley model can be used to evaluate the management practices on barley crop. The current research was carried out to evaluate the APSIM-Barley model in relation to simulating the growth, development, and yield of three barley cultivars (Azaran, Jolgeh, and Bahman) under different conditions of nitrogen and irrigation supplies. Materials and methods In order to evaluate the APSIM-Barley model regarding simulating and quantifying the phenological stages, biomass, and grain yield of different barley cultivars, some independent experiment datasets were used. For crop model calibration, an experiment was conducted in a factorial split plot design at the Agricultural Research Center of Hamedan in 2019. The main factor consisted of three irrigation levels (30-40, 60-70, and 90-100% of the field capacity) and sub-factors included cultivar levels (Azran, Jolgeh, and Bahman as early, mid, and late cultivars, respectively) and three nitrogen levels (zero, medium, and optimum). For model validation, another series of independent datasets in different years consisting of published articles and research projects were used. For assessing the efficiency of the crop model and comparing the simulated and measured values, R2, nRMSE, d-index, and MBE indices were used and OriginPro software was considerd for all statistical analysis and drawing of figures.Results and Discussion The simulation results of APSIM-Barley model in the calibration step showed that nRMSE for days to flowering and maturity was 2.43 and 2.4%, respectively. Also, under full water and nitrogen conditions, nRMSE for the leaf area index of Azaran, Jolgeh and Bahman cultivars was 13.5, 14.1, and 10.7%, respectively, and under severe water and nitrogen stresses, it was 16.5, 18.6, and 26.5% respectively. At this step, and nRMSE and d-index for biomass and grain yield were 24.9% and 0.98 and 15.2% and 0.96, respectively. In model validation step, nRMSE, d-index, R2, MBE were 15.02%, 0.96, 0.82 and 0.34 t ha-1, respectively, for grain yield. ConclusionIn general, the results showed that the APSIM-Barley model was able to simulate the growth, development, and grain yield of different barley cultivars with acceptable accuracy under different water and nitrogen management conditions in different years and regions. Therefore, the APSIM-Barley model can be used as a reliable tool in future studies such as climate change assessment, yield gap analysis, agricultural zoning, and etc. by other researchers..

Identification of different drought patterns of dryland wheat in the northwest of Iran by APSIM model

Sajjad Rahimi-Moghaddam; Hamed Eyni-Nargeseh

Volume 45, Issue 3 , December 2022, , Pages 435-446

https://doi.org/10.22055/ppd.2022.40869.2036

Abstract

IntroductionImproving crop yields can meet the projected demand in developed and developing countries as a key and promising solution. To increase dryland wheat production in arid and ... Read More IntroductionImproving crop yields can meet the projected demand in developed and developing countries as a key and promising solution. To increase dryland wheat production in arid and semi-arid regions such as Iran, researchers and crop breeders need to understand the drought pattern (in terms of time and intensity) occurring in wheat agro-systems because depending on the time and intensity of drought stress, drought stress impacts on different processes and genes of crops. To accurately study the drought pattern of dryland wheat and different cultivars adapted to these conditions, researchers need to design multi-environmental field experiments, which are time-consuming and costly. Under these circumstances, some essential information is limited in the field experiments, especially when done for only a few years, and basically does not indicate fluctuations in the target environments. In contrast, the modeling and simulation approach is a useful method for comprehensive environmental impact assessment. Materials and MethodsThis study was conducted to identify and study different drought patterns in 8 locations including Ardebil, Khalkhal, Maragheh, Marand, Meshginshahr, Sarab, Tabriz, and Urmia in the northwest of Iran. APSIM model was used to simulate the growth and development of dryland wheat in the study locations. The long-term climatic included minimum and maximum temperatures, rainfall, and radiation (1980 to 2016) collected from the Meteorological Organization of Iran. These data were used as input to the crop growth simulation model. Water supply and demand index was used to determine different drought patterns. This index is obtained by dividing soil water content to plant water demand (WSDR). After calculating the water supply and demand index for each year and region, the CLARA clustering method was used to classify and group the index. The number of groups obtained by CLARA method was equal to the type of drought patterns of dryland wheat. Results and DiscussionThe results showed 4 different drought patterns during the dryland wheat growing season in the study locations. Drought pattern 1 showed no drought stress or mild drought stress throughout the growing season of dryland wheat. Under drought pattern 2, drought stress started before flowering and ended during seed filling. For drought pattern 3, water stress started from the time of germination but removed during the vegetative growth period. Drought pattern 4 started in the early stages of growth and continued until the end of the dryland wheat growing season. The frequency of occurrence of 1, 2, 3, and 4 drought patterns was 27.8%, 26%, 8% and 38.2%, respectively. On average across locations, transpiration was 160, 137.1, 31, and 86.7 mm for 1, 2, 3, and 4 drought patterns during the wheat growing season. Also, a significant difference was observed among different drought patterns in terms of biomass that biomass under drought patterns 1, 2, 3, and 4 was equal to 13411, 10076, 2097 and, 6435 kg ha-1, respectively. Grain yield under drought pattern types 1, 2, 3 and 4 were 3738.7, 2949.7, 694.6, and 1456.2 kg ha-1, respectively. The highest percentage of light and mild drought patterns (1 and 2) was related to Marand region with 100% and Sarab region experienced more severe drought patterns (3 and 4) than other regions (88.2%). Under different locations and drought patterns, on average across years, the highest grain yield was simulated for Marand under drought pattern 1 (4759 kg ha-1) and the lowest was recorded for Urmia under drought pattern 3 (208.8 kg ha-1). ConclusionIn general, the simulation results showed that 4 different types of drought patterns were observed during the dryland wheat growing period in the study locations. The frequency of occurrence of 4 drought patterns was various in the study locations and the grain yield varied under the drought patterns. However, the highest occurrence was related to drought pattern 4, under which drought stress begun in the early stages of growth and lasted until the end of the dryland wheat growing season. Accordingly, it is suggested to consider various strategies such as using cultivars with short maturity period to avoid drought stress at the end of the season and using cultivars with high resistance to drought stress (especially in the flowering period).

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Author = Rahimi-Moghaddam, Sajjad

Evaluation of withholding irrigation on different canola cultivars under autumn cultivation conditions: a simulation study

Abstract

Simulating response of lentil cultivars to sowing date and initial soil moisture in Lorestan province

Abstract

Evaluating APSIM-Barley model for different barley cultivars under water and nitrogen stresses

Abstract

Identification of different drought patterns of dryland wheat in the northwest of Iran by APSIM model

Abstract