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Conventional rice farming with application of continuous flooding is not essential to achieve good yield and is known as a major source of greenhouse gas emissions from paddy fields. Hence, system of rice intensification (SRI) is proposed as an alternative of rice farming with more efficient water use for producing more rice and reducing greenhouse gas emissions. The main challenge in the application of SRI is finding the optimal water management to raise yield and water productivity and to reduce greenhouse gas emissions simultaneously. To support this purpose, improving the technology for collecting precise field data is important through continuous measurements of related variables by a field monitoring system (FMS). Therefore, this study was conducted to achieve this purpose based on the FMS data. Nine chapters were presented in this study. In chapter 1, the introduction presented the originality and the main objectives of this study. The main objectives were to develop and evaluate the FMS for SRI paddy field with different irrigation regimes, to identify effects of irrigation regime on yield and water productivity and greenhouse gas emissions, and then to find the optimal irrigation regime for maximizing yield and water productivity and reducing greenhouse gas emissions. In chapter 2, a method of data acquisition was presented. Here, we used the FMS consisting of a FieldRouter equipped with a surveillance camera and connected to meteorological and soil data loggers. The meteorological data consisted of solar radiation, air temperature, relative humidity, wind speed, and precipitation, while soil data consisted of soil moisture and soil temperature. The FieldRouter was set to automatically work from 12:00 to 12:30 PM (local time) regulated by a timer to collect the data, and then to send the data as well as a plant image to the data server through the GSM connection. The FMS was installed in Nusantara Organic SRI Center (NOSC), Nagrak, Sukabumi, West Java, Indonesia. Four SRI paddy plots under different irrigation regimes were monitored by the FMS. The FMS was demonstrated to be effective, efficient and reliable in monitoring the plots during 2010-2012. The actual field conditions were monitored well in terms of image, numeric and graphic data. The data were then used for further analyses to find the optimal SRI water management. In chapter 3, neural network (NN) models were proposed to estimate soil moisture based on meteorological data. Sometimes during the above monitoring period, some soil moisture data were lost by unexpected problems in the field, where the sensor was broken, the cable was unplugged or the data logger battery was depleted. Therefore, the motivation of this chapter was to solve the problems. We developed two NN models; the first model was developed to estimate reference evapotranspiration (ETo) according to maximum, average, and minimum values of air temperature and solar radiation; the second model was to estimate soil moisture according to the estimated ETo and precipitation. As the results of NN performance, ETo was accurately estimated by the first NN model with R2 values of 0.95 and 0.92 (p\u003c0.01) and mean squared deviation (MSD) of 0.02 and 0.24 mm for training and validation processes, respectively. Then, the second model estimated soil moisture with R2 values of greater than 0.70 (p\u003c0.01) and MSD of lower than 3x10-4 cm3/cm3 for the processes. Thus, the tight correlations between observed and estimated values of soil moisture were generated by the models. Water balance variables such as irrigation water, runoff, percolation and crop evapotranspiration (ETc) are required to evaluate effects of irrigation regime on yield and water productivity. However, the variables were not easily measured in the fields. In chapter 4, a linear program with Excel Solver method was proposed to estimate the variables based on the monitored and estimated data in chapters 2 and 3. When indirect validation was used, the method was reliable as indicated by R2 values of greater than 0.90 (p\u003c0.01) between observed and calculated values of soil moisture. As supporting evidences of the model reliability, a significant linear correlation of R2\u003e0.97 (p\u003c0.01) was recognized between precipitation and estimated runoff. Also, a well-matching relationship between the total inflow and outflow was observed for all irrigation regimes. In chapter 5, we used the estimated ETc in chapter 4 to determine crop coefficient (Kc) values for the SRI paddy. Usually, Kc value is estimated by using the lysimeter method. But the method is time consuming and expensive for equipment preparation. Therefore, we proposed a simple method using a linear program with Excel Solver to estimate ETc, and then the estimated ETc was used to determine Kc value. Here, we evaluated the method by comparing the estimated ETc and the ETc derived from the FAO procedure. The result showed that the estimated ETc had a highly significant correlation to the ETc derived from the FAO procedure. Then, the Kc value was well determined using the estimated ETc. The Kc gradually increased in the initial and crop development stages, and it reached a peak in the mid-season stage. Then, the Kc declined rapidly in the late season stage. The Kc trend agreed with the typical Kc trend described by the FAO procedure for most crops. In chapter 6, water management in SRI was optimized by a genetic algorithm (GA) model to maximize yield and water productivity based on the monitored and estimated data in chapters 2, 3, and 4. Before performing optimization, a formula to describe yield by plant growth parameters was identified using multiple linear regression analysis. Then, the plant growth parameters were estimated by the NN model using the soil moisture data set. The results showed that plant growth and yield were clearly affected by irrigation regime. Then, according to the identification results, the optimal irrigation regime was represented by the optimal combination of soil moisture levels for the growth stages. The GA model recommended the optimal combination of soil moisture levels of 0.622, 0.563, 0.522, and 0.350 cm3/cm3 for the initial, crop development, mid-season, and late season growth stages, respectively. By this scenario, it was estimated that the yield can be increased up to 6.33% and water productivity up to 25.09% with water saving up to 12.71%. In chapter 7, since the FMS was not yet equipped with greenhouse gas sensors and their data loggers, experiments to investigate effects of irrigation regime on greenhouse gas emissions were carried out separately in the greenhouse of Meiji University in Kanagawa Prefecture, Japan. There were two experiments, i.e., using lysimeters and pots with three different regimes and two replications in each experiment. We called the regimes as continuous flooding, combination, and intermittent drainage regimes for the lysimeter experiment, while wet, medium, and dry regimes for the pot experiment. As the results of both experiments, greenhouse gas emissions were clearly affected by water management. The combination regime was found to be the best strategy for mitigation of greenhouse gas emissions achieving a greater rice yield than the others, with water saving up to 16.92%. Meanwhile, in the pot experiment, the dry regime was the best for the mitigation, but this regime resulted in a lower yield than the wet regime. All of the findings of this study were discussed in chapter 8. By adopting quasireal time monitoring, the developed FMS was more power saving and Internet cost effective than real time monitoring. The field data were collected properly and could be used to find the optimal SRI water management. The proposed methods were reliable as indicated by their acceptable performances. The optimal SRI water management was the combination of soil moisture levels of wet, wet, medium, and dry for the initial, crop development, mid-season and late season stages, respectively. We called this regime as W-W-M-D regime. In the initial and crop development stages, the field should be kept at the wet level because the plants need enough water to meet their requirements for optimally developing root, stem, leaf and tiller. Meanwhile, in the mid-season stage the field should be drained to make the field in the medium level when plants are focusing on their reproductive period to avoid spikelet sterility. The medium level in the midseason stage was also supposed to be an affective option to reduce greenhouse gas emissions from paddy field. This argument was supported by the result of lysimeter experiment (chapter 7) in which combination regime with the application of mid-season drainage produced the highest yield and water productivity and contributed lowest emission among the regimes. The lowest emission was caused by reducing methane emission significantly when the water was drained in the mid-season stage. Finally, in the last season stage, the field should be drained to make the soil in the dry level to save water because all plant organs have perfectly developed. Finally, in chapter 9, we drew conclusions from the results described in the previous chapters. Based on the field experiments, the FMS was effective, efficient and reliable in monitoring SRI paddy field in Indonesia in long-term experiments. We found that the optimal SRI water management was W-W-M-D regime in maximizing yield and water productivity and supposed releasing the lowest amount of emission. For further studies, the utilization of FMS for SRI paddy fields may be enforced with greenhouse gas sensors and their data loggers. 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Optimizing Water Management in System of Rice Intensification Paddy Fields by Field Monitoring Technology
https://doi.org/10.15083/00005441
https://doi.org/10.15083/000054415b3b59cc-1742-4265-bebc-8e2cb220d634
名前 / ファイル | ライセンス | アクション |
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H24_3937_Arif.pdf (5.8 MB)
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Item type | 学位論文 / Thesis or Dissertation(1) | |||||
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公開日 | 2013-11-14 | |||||
タイトル | ||||||
タイトル | Optimizing Water Management in System of Rice Intensification Paddy Fields by Field Monitoring Technology | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源 | http://purl.org/coar/resource_type/c_46ec | |||||
タイプ | thesis | |||||
ID登録 | ||||||
ID登録 | 10.15083/00005441 | |||||
ID登録タイプ | JaLC | |||||
その他のタイトル | ||||||
その他のタイトル | フィールドモニタリング技術によるSRI水田の最適水管理に関する研究 | |||||
著者 |
Arif, Chusnul
× Arif, Chusnul |
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著者別名 | ||||||
識別子 | 11377 | |||||
識別子Scheme | WEKO | |||||
姓名 | アリフ, クスヌル | |||||
著者所属 | ||||||
著者所属 | 東京大学大学院農学生命科学研究科農学国際専攻 | |||||
Abstract | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Water consumption and greenhouse gas emissions have emerged as major issues in rice production. Conventional rice farming with application of continuous flooding is not essential to achieve good yield and is known as a major source of greenhouse gas emissions from paddy fields. Hence, system of rice intensification (SRI) is proposed as an alternative of rice farming with more efficient water use for producing more rice and reducing greenhouse gas emissions. The main challenge in the application of SRI is finding the optimal water management to raise yield and water productivity and to reduce greenhouse gas emissions simultaneously. To support this purpose, improving the technology for collecting precise field data is important through continuous measurements of related variables by a field monitoring system (FMS). Therefore, this study was conducted to achieve this purpose based on the FMS data. Nine chapters were presented in this study. In chapter 1, the introduction presented the originality and the main objectives of this study. The main objectives were to develop and evaluate the FMS for SRI paddy field with different irrigation regimes, to identify effects of irrigation regime on yield and water productivity and greenhouse gas emissions, and then to find the optimal irrigation regime for maximizing yield and water productivity and reducing greenhouse gas emissions. In chapter 2, a method of data acquisition was presented. Here, we used the FMS consisting of a FieldRouter equipped with a surveillance camera and connected to meteorological and soil data loggers. The meteorological data consisted of solar radiation, air temperature, relative humidity, wind speed, and precipitation, while soil data consisted of soil moisture and soil temperature. The FieldRouter was set to automatically work from 12:00 to 12:30 PM (local time) regulated by a timer to collect the data, and then to send the data as well as a plant image to the data server through the GSM connection. The FMS was installed in Nusantara Organic SRI Center (NOSC), Nagrak, Sukabumi, West Java, Indonesia. Four SRI paddy plots under different irrigation regimes were monitored by the FMS. The FMS was demonstrated to be effective, efficient and reliable in monitoring the plots during 2010-2012. The actual field conditions were monitored well in terms of image, numeric and graphic data. The data were then used for further analyses to find the optimal SRI water management. In chapter 3, neural network (NN) models were proposed to estimate soil moisture based on meteorological data. Sometimes during the above monitoring period, some soil moisture data were lost by unexpected problems in the field, where the sensor was broken, the cable was unplugged or the data logger battery was depleted. Therefore, the motivation of this chapter was to solve the problems. We developed two NN models; the first model was developed to estimate reference evapotranspiration (ETo) according to maximum, average, and minimum values of air temperature and solar radiation; the second model was to estimate soil moisture according to the estimated ETo and precipitation. As the results of NN performance, ETo was accurately estimated by the first NN model with R2 values of 0.95 and 0.92 (p<0.01) and mean squared deviation (MSD) of 0.02 and 0.24 mm for training and validation processes, respectively. Then, the second model estimated soil moisture with R2 values of greater than 0.70 (p<0.01) and MSD of lower than 3x10-4 cm3/cm3 for the processes. Thus, the tight correlations between observed and estimated values of soil moisture were generated by the models. Water balance variables such as irrigation water, runoff, percolation and crop evapotranspiration (ETc) are required to evaluate effects of irrigation regime on yield and water productivity. However, the variables were not easily measured in the fields. In chapter 4, a linear program with Excel Solver method was proposed to estimate the variables based on the monitored and estimated data in chapters 2 and 3. When indirect validation was used, the method was reliable as indicated by R2 values of greater than 0.90 (p<0.01) between observed and calculated values of soil moisture. As supporting evidences of the model reliability, a significant linear correlation of R2>0.97 (p<0.01) was recognized between precipitation and estimated runoff. Also, a well-matching relationship between the total inflow and outflow was observed for all irrigation regimes. In chapter 5, we used the estimated ETc in chapter 4 to determine crop coefficient (Kc) values for the SRI paddy. Usually, Kc value is estimated by using the lysimeter method. But the method is time consuming and expensive for equipment preparation. Therefore, we proposed a simple method using a linear program with Excel Solver to estimate ETc, and then the estimated ETc was used to determine Kc value. Here, we evaluated the method by comparing the estimated ETc and the ETc derived from the FAO procedure. The result showed that the estimated ETc had a highly significant correlation to the ETc derived from the FAO procedure. Then, the Kc value was well determined using the estimated ETc. The Kc gradually increased in the initial and crop development stages, and it reached a peak in the mid-season stage. Then, the Kc declined rapidly in the late season stage. The Kc trend agreed with the typical Kc trend described by the FAO procedure for most crops. In chapter 6, water management in SRI was optimized by a genetic algorithm (GA) model to maximize yield and water productivity based on the monitored and estimated data in chapters 2, 3, and 4. Before performing optimization, a formula to describe yield by plant growth parameters was identified using multiple linear regression analysis. Then, the plant growth parameters were estimated by the NN model using the soil moisture data set. The results showed that plant growth and yield were clearly affected by irrigation regime. Then, according to the identification results, the optimal irrigation regime was represented by the optimal combination of soil moisture levels for the growth stages. The GA model recommended the optimal combination of soil moisture levels of 0.622, 0.563, 0.522, and 0.350 cm3/cm3 for the initial, crop development, mid-season, and late season growth stages, respectively. By this scenario, it was estimated that the yield can be increased up to 6.33% and water productivity up to 25.09% with water saving up to 12.71%. In chapter 7, since the FMS was not yet equipped with greenhouse gas sensors and their data loggers, experiments to investigate effects of irrigation regime on greenhouse gas emissions were carried out separately in the greenhouse of Meiji University in Kanagawa Prefecture, Japan. There were two experiments, i.e., using lysimeters and pots with three different regimes and two replications in each experiment. We called the regimes as continuous flooding, combination, and intermittent drainage regimes for the lysimeter experiment, while wet, medium, and dry regimes for the pot experiment. As the results of both experiments, greenhouse gas emissions were clearly affected by water management. The combination regime was found to be the best strategy for mitigation of greenhouse gas emissions achieving a greater rice yield than the others, with water saving up to 16.92%. Meanwhile, in the pot experiment, the dry regime was the best for the mitigation, but this regime resulted in a lower yield than the wet regime. All of the findings of this study were discussed in chapter 8. By adopting quasireal time monitoring, the developed FMS was more power saving and Internet cost effective than real time monitoring. The field data were collected properly and could be used to find the optimal SRI water management. The proposed methods were reliable as indicated by their acceptable performances. The optimal SRI water management was the combination of soil moisture levels of wet, wet, medium, and dry for the initial, crop development, mid-season and late season stages, respectively. We called this regime as W-W-M-D regime. In the initial and crop development stages, the field should be kept at the wet level because the plants need enough water to meet their requirements for optimally developing root, stem, leaf and tiller. Meanwhile, in the mid-season stage the field should be drained to make the field in the medium level when plants are focusing on their reproductive period to avoid spikelet sterility. The medium level in the midseason stage was also supposed to be an affective option to reduce greenhouse gas emissions from paddy field. This argument was supported by the result of lysimeter experiment (chapter 7) in which combination regime with the application of mid-season drainage produced the highest yield and water productivity and contributed lowest emission among the regimes. The lowest emission was caused by reducing methane emission significantly when the water was drained in the mid-season stage. Finally, in the last season stage, the field should be drained to make the soil in the dry level to save water because all plant organs have perfectly developed. Finally, in chapter 9, we drew conclusions from the results described in the previous chapters. Based on the field experiments, the FMS was effective, efficient and reliable in monitoring SRI paddy field in Indonesia in long-term experiments. We found that the optimal SRI water management was W-W-M-D regime in maximizing yield and water productivity and supposed releasing the lowest amount of emission. For further studies, the utilization of FMS for SRI paddy fields may be enforced with greenhouse gas sensors and their data loggers. Then, the optimal water management can be determined not only for maximizing yield and water productivity but also for reducing greenhouse gas emissions. | |||||
書誌情報 | 発行日 2013-03-25 | |||||
学位名 | ||||||
学位名 | 博士(農学) | |||||
学位 | ||||||
値 | doctoral | |||||
学位分野 | ||||||
Agriculture(農学) | ||||||
学位授与機関 | ||||||
学位授与機関名 | University of Tokyo (東京大学) | |||||
研究科・専攻 | ||||||
Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences(農学生命科学研究科農学国際専攻) | ||||||
学位授与年月日 | ||||||
学位授与年月日 | 2013-03-25 | |||||
学位記番号 | ||||||
博農第3937号 |