Synchronizing Nitrogen Fertilization and Planting Date to Improve Resource Use Efficiency, Productivity, and Profitability of Upland Rice

Synchronizing nitrogen (N) fertilization with planting date (PD) could enhance resource use efficiency and profitability of upland rice (Oryza sativa L.) production in Thailand. The objective of the study was to assess upland rice responses to four N fertilization rates (NFRs) and three planting dates. Field experiments were conducted during two growing seasons under four NFRs, no N applied (N0), 30 (N30), 60 (N60), and 90 kg N ha−1 (N90), and NFR were applied at the initiation of tillering and panicle emergence stages. The planting dates selected were early (PD1), intermedium (PD2), and late planting (PD3) between September and December of each season. The NFRs and planting dates had a significant influence on N uptake, N use efficiency (NUE), crop water productivity, yield and yield attributes, and profitability of upland rice production. A linear relationship among NFRs, agronomic traits of upland rice, N uptake, and crop water productivity was observed, and a significant seasonal effect was indicated. Fertilization at N90 under PD2 enhanced yields, yield attributes, and grain yields, as well as crop water productivity by 56 and 105% during the second and first seasons, respectively. Grain N, total N, and straw N were increased by 159, 159, and 160%, and by 90, 114, and 153%, during the first and second seasons, respectively. Enhanced N efficiencies, including agronomic efficiency, recovery efficiency, partial factor productivity, and N harvest index, at varying NFRs were observed under PD2 during both seasons. Highly significant (p < 0.001) and positive associations were observed among agronomic attributes, N uptake, NUE, and crop water productivity of upland rice in correlation assessment. Profitability from grain yields was observed with N fertilization and N90 resulted in maximum profit under all the PDs. However, the highest marginal benefit-cost ratio was observed at N60 under PD2 during both seasons. The results suggest that the NFR of 90 kg N ha−1 and planting at the end of September or start of October would enhance resource use efficiency and productivity, and maximize profitability. Furthermore, long–term field investigations with a range of NFRs and adopting forecasting measures to adjust the planting date for upland rice are recommended

การประเมินผลกระทบของความเครียดจากน้ำต่อองค์ประกอบชีวมวลของข้าว ความพร้อมของวัตถุดิบ และศักยภาพในการผลิตพลังงานชีวภาพ

Master of Engineering (Energy Technology), 2022Bioenergy production from rice biomass feedstock is considered as one of the potential clean energy resources and several small biomass−based powerplants has been established in rice growing areas in addition to use of rice biomass as supplementary feedstock. However, rice biomass production is significantly affected due to various factors including climatic factors, shift in rice production systems, choice of cultivars at large scale cultivation, variability in biomass production potential and water stress occurrence which results in declined biomass availability and quality. Water stress is a critical aspect which influence the rice biomass productivity and quality the most, therefore, the impacts of water stress were evaluated on six Thai rice cultivars for their biomass quality, production, and bioenergy potential. Rice cultivars were experimented in field under well−watered (WW) and water stress (WS) treatments. Data for days to maturity of rice cultivars and rice biomass contributing parameters including stem height, stem numbers and biomass yield was collected at harvest. Proximate and lignocellulosic contents of rice biomass samples were determined for biomass composition analysis. Results showed that water stress negatively affected the crop production performance which resulted in 11−41% decline in biomass yield. Cultivar stability assessment for stable biomass production indicated that cultivars Hom Pathum and Dum Ja demonstrated comparatively smaller reductions by 11% in their biomass yield production under water stress. Statistical comparison for proximate contents showed significant negative affect which influenced biomass quality as the ash contents of cultivars Hom Chan, Dum Ja and RD−15 were raised by 4−29% under water stress. Lignocellulosic evaluation revealed, an increase in lignin contents of cultivars Hom Nang Kaew, Hom Pathum, Dum Ja and RD−15 ranging from 7 to 39%. Decline in biomass production under water stress caused a 10−42% reduction in bioenergy potential of Thai rice cultivars. Results demonstrated that cultivation of stress prone rice cultivars or farmer’s choice to grow specific cultivars and incidence of water stress during rice crop growth period will reduce biomass production potential, biomass feedstock availability to biomass−based powerplants and will affect powerplant’s energy conversion efficiency leading to declined bioenergy production.1. Faculty of Engineering, Prince of Songkla University 2. Graduate School, Prince of Songkla University for thesis research funding for topic on community problem solvin

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand for the food are the hot discussions these days. Keeping in view the importance of the abovementioned issues, this manuscript summarizes the modern approaches of IoT and smart techniques to aid sustainable crop production. The study also demonstrates the benefits of using modern IoT approaches and smart techniques in the establishment of smart- and resource-use-efficient farming systems. Modern technology not only aids in sustaining productivity under limited resources, but also can help in observing climatic variations, monitoring soil nutrients, water dynamics, supporting data management in farming systems, and assisting in insect, pest, and disease management. Various type of sensors and computer tools can be utilized in data recording and management of cropping systems, which ensure an opportunity for timely decisions. Digital tools and camera-assisted cropping systems can aid producers to monitor their crops remotely. IoT and smart farming techniques can help to simulate and predict the yield production under forecasted climatic conditions, and thus assist in decision making for various crop management practices, including irrigation, fertilizer, insecticide, and weedicide applications. We found that various neural networks and simulation models could aid in yield prediction for better decision support with an average simulation accuracy of up to 92%. Different numerical models and smart irrigation tools help to save energy use by reducing it up to 8%, whereas advanced irrigation helped in reducing the cost by 25.34% as compared to soil-moisture-based irrigation system. Several leaf diseases on various crops can be managed by using image processing techniques using a genetic algorithm with 90% precision accuracy. Establishment of indoor vertical farming systems worldwide, especially in the countries either lacking the supply of sufficient water for the crops or suffering an intense urbanization, is ultimately helping to increase yield as well as enhancing the metabolite profile of the plants. Hence, employing the advanced tools, a modern and smart agricultural farming system could be used to stabilize and enhance crop productivity by improving resource use efficiency of applied resources i.e., irrigation water and fertilizers

Impact of Nitrogen Application Rates on Upland Rice Performance, Planted under Varying Sowing Times

Application of suitable nitrogen (N) fertilizer application rate (NR) with respect to sowing time (ST) could help to maximize the performance and productivity of upland rice in Southern Thailand. The 2-year experiments were conducted in the sheds to evaluate the agronomic responses of the upland rice genotype, Dawk Pa–yawm, under various combinations of NR and ST between 2018–2019 and 2019–2020 aimed at obtaining sufficient research evidence for the improved design of long-term field trials in Southern Thailand. As with the initial research, four NR were applied as N0 with no applied N, 1.6 g N pot−1, 3.2 g N pot−1 and 4.8 g N pot−1, and experiments were grown under three ST including early (ST1), medium (ST2) and late sowing (ST3). Results from the experiments indicate that the application of 4.8 g N pot−1 resulted in maximum grain yield under all ST in both years. However, a maximum increase in grain yield was observed under ST2 by 54–101% in 2018–2019 and by 276–339% in 2019–2020. Maximum grain N uptake of 0.57 and 0.82 g pot−1 was also observed at NR 4.8 g N pot−1 under ST2 in both years, respectively. Application of NR 4.8 g N pot−1 resulted in the highest N agronomic efficiency (NAE), nitrogen use efficiency (NUE) and water use efficiency (WUE). However, the performance of yield and yield attributes, N uptake, N use efficiencies and WUE were declined in late sowing (ST3). Significant positive association among yield, yield attributes, N uptake and WUE indicated that an increase in NR up to 4.8 g N pot−1 improved the performance of Dawk Pa–yawm. The results suggest that the application of 4.8 g N pot−1 (90 kg N ha−1) for upland rice being grown during September (ST2) would enhance N use efficiencies, WUE and ultimately improve the yield of upland rice. However, field investigations for current study should be considered prior to general recommendations. Moreover, based on the findings of this study, the importance of variable climatic conditions in the field, and the variability in genotypic response to utilize available N and soil moisture, authors suggest considering more levels of NR and intervals for ST with a greater number of upland rice genotypes to observe variations in field experiments for the precise optimization of NR according to ST

Potential Impacts of Water Stress on Rice Biomass Composition and Feedstock Availability for Bioenergy Production

Bioenergy from rice biomass feedstock is considered one of the potential clean energy resources and several small biomass-based powerplants have been established in rice–growing areas of Thailand. Rice production is significantly affected by drought occurrence which results in declined biomass production and quality. The impact of water stress (WS) was evaluated on six rice cultivars for biomass quality, production and bioenergy potential. Rice cultivars were experimented on in the field under well–watered (WW) and WS conditions. Data for biomass contributing parameters were collected at harvest whereas rice biomass samples were analyzed for proximate and lignocellulosic contents. Results indicated that WS negatively influenced crop performance resulting in 11–41% declined biomass yield (BY). Stability assessment indicated that cultivars Hom Pathum and Dum Ja were stress–tolerant as they exhibited smaller reductions by 11% in their BY under WS. Statistics for proximate components indicated a significant negative impact influencing biomass quality as ash contents of Hom Chan, Dum Ja and RD-15 were increased by 4–29%. Lignocellulosic analysis indicated, an increase in lignin contents of Hom Nang Kaew, Hom Pathum, Dum Ja and RD–15 ranging 7–39%. Reduced biomass production resulted in a 10–42% reduction in bioenergy potential (E). Results proved that cultivation of stress-susceptible cultivars or farmer’s choice and occurrence of WS during crop growth will reduce biomass production, biomass feedstock availability to biomass-based powerplants and affect powerplant’s conversion efficiency resulting in declined bioenergy production

Responses of Lowland Rice Genotypes under Terminal Water Stress and Identification of Drought Tolerance to Stabilize Rice Productivity in Southern Thailand

Lowland rice is an important cereal crop that plays a key role in the food security and the economy of Thailand. Terminal water stress (TWS) in rainfed lowland areas poses threats to rice productivity due to stress occurrence at terminal crop stages and extreme sensitivity of rice to TWS. A two-year study was conducted to characterize the performance of yield and yield attributes of twelve Thai lowland rice genotypes under TWS, to identify stress-tolerant genotypes using stress response indices and to identify promising stress indices which are correlated with grain yield (GY) under well-watered (WW) and TWS conditions for their use as rapid identifiers in a rice crop breeding program for enhancing drought stress tolerance. Measurements were recorded under WW and TWS conditions. Highly significant variations were observed amongst assessed genotypes for their yield productivity responses. According to stress response indices, genotypes were categorized into stress-tolerant and stress susceptible genotypes. Genotypes Hom Pathum, Sang Yod, Dum Ja and Pathum Thani-1 were found highly stress tolerant and relatively high yielding; genotypes Look Pla and Lep Nok were stress tolerant, whereas genotypes Chor Lung, Hom Nang Kaew and Hom Chan were moderately tolerant genotypes. Hence, stress-tolerant genotypes could be potentially used for cultivation under rainfed and water-limited conditions, where TWS is predicted particularly in southern Thailand to stabilize rice productivity. Stress tolerance indices, including stress tolerance index (STI), geometric mean productivity (GMP), mean productivity index (MPRO) and harmonic mean index (MHAR), indicated strong and positive associations with GY under WW and TWS; thus, these indices could be used to indicate stress tolerance in rice crop breeding program aimed at a rapid screening of lowland rice genotypes for stress tolerance

Impact of Nitrogen Application Rates on Upland Rice Performance, Planted under Varying Sowing Times

Application of suitable nitrogen (N) fertilizer application rate (NR) with respect to sowing time (ST) could help to maximize the performance and productivity of upland rice in Southern Thailand. The 2-year experiments were conducted in the sheds to evaluate the agronomic responses of the upland rice genotype, Dawk Pa&ndash;yawm, under various combinations of NR and ST between 2018&ndash;2019 and 2019&ndash;2020 aimed at obtaining sufficient research evidence for the improved design of long-term field trials in Southern Thailand. As with the initial research, four NR were applied as N0 with no applied N, 1.6 g N pot&minus;1, 3.2 g N pot&minus;1 and 4.8 g N pot&minus;1, and experiments were grown under three ST including early (ST1), medium (ST2) and late sowing (ST3). Results from the experiments indicate that the application of 4.8 g N pot&minus;1 resulted in maximum grain yield under all ST in both years. However, a maximum increase in grain yield was observed under ST2 by 54&ndash;101% in 2018&ndash;2019 and by 276&ndash;339% in 2019&ndash;2020. Maximum grain N uptake of 0.57 and 0.82 g pot&minus;1 was also observed at NR 4.8 g N pot&minus;1 under ST2 in both years, respectively. Application of NR 4.8 g N pot&minus;1 resulted in the highest N agronomic efficiency (NAE), nitrogen use efficiency (NUE) and water use efficiency (WUE). However, the performance of yield and yield attributes, N uptake, N use efficiencies and WUE were declined in late sowing (ST3). Significant positive association among yield, yield attributes, N uptake and WUE indicated that an increase in NR up to 4.8 g N pot&minus;1 improved the performance of Dawk Pa&ndash;yawm. The results suggest that the application of 4.8 g N pot&minus;1 (90 kg N ha&minus;1) for upland rice being grown during September (ST2) would enhance N use efficiencies, WUE and ultimately improve the yield of upland rice. However, field investigations for current study should be considered prior to general recommendations. Moreover, based on the findings of this study, the importance of variable climatic conditions in the field, and the variability in genotypic response to utilize available N and soil moisture, authors suggest considering more levels of NR and intervals for ST with a greater number of upland rice genotypes to observe variations in field experiments for the precise optimization of NR according to ST

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand for the food are the hot discussions these days. Keeping in view the importance of the abovementioned issues, this manuscript summarizes the modern approaches of IoT and smart techniques to aid sustainable crop production. The study also demonstrates the benefits of using modern IoT approaches and smart techniques in the establishment of smart- and resource-use-efficient farming systems. Modern technology not only aids in sustaining productivity under limited resources, but also can help in observing climatic variations, monitoring soil nutrients, water dynamics, supporting data management in farming systems, and assisting in insect, pest, and disease management. Various type of sensors and computer tools can be utilized in data recording and management of cropping systems, which ensure an opportunity for timely decisions. Digital tools and camera-assisted cropping systems can aid producers to monitor their crops remotely. IoT and smart farming techniques can help to simulate and predict the yield production under forecasted climatic conditions, and thus assist in decision making for various crop management practices, including irrigation, fertilizer, insecticide, and weedicide applications. We found that various neural networks and simulation models could aid in yield prediction for better decision support with an average simulation accuracy of up to 92%. Different numerical models and smart irrigation tools help to save energy use by reducing it up to 8%, whereas advanced irrigation helped in reducing the cost by 25.34% as compared to soil-moisture-based irrigation system. Several leaf diseases on various crops can be managed by using image processing techniques using a genetic algorithm with 90% precision accuracy. Establishment of indoor vertical farming systems worldwide, especially in the countries either lacking the supply of sufficient water for the crops or suffering an intense urbanization, is ultimately helping to increase yield as well as enhancing the metabolite profile of the plants. Hence, employing the advanced tools, a modern and smart agricultural farming system could be used to stabilize and enhance crop productivity by improving resource use efficiency of applied resources i.e., irrigation water and fertilizers

Impacts of Drought Stress on Water Use Efficiency and Grain Productivity of Rice and Utilization of Genotypic Variability to Combat Climate Change

Rice is an important cereal and drought stress is a critical abiotic stress that negatively influences the performance and productivity of rice crop, particularly under a changing climate scenario. The objectives of this study were to evaluate the impacts of drought stress on grain productivity and water use efficiency of rice cultivars and to assess the genotypic variability among the tested cultivars. Two irrigation treatments including a control and drought stress were applied to the experiments during 2018&ndash;2019 and 2019&ndash;2020. The statistical evaluation included a comparison of means, genotypic and phenotypic coefficients of variation, path analysis, correlation assessment, hierarchical clustering of tested cultivars and principal component analysis. The results indicated that drought stress negatively affected the grain productivity of the rice cultivars. The grain productivity of the cultivars decreased, ranging between 21&ndash;45% and 21&ndash;52% in the first and second season, respectively. Similarly, water use efficiency was significantly decreased ranging between 7&ndash;53% and 21&ndash;55% during the first and the second season, respectively. The broad-sense heritability for grain productivity was differed under control and drought stress treatment, indicating that the chances of the transfer of grain-productivity-related traits could be affected during selection for stress tolerance. The correlation assessment indicated that the intensity of association among the evaluated parameters was higher under the control treatment. A maximum direct effect was observed by water consumption (1.76) under control whereas, by water use efficiency (1.09) under drought stress treatment on grain productivity in path analysis. Considering the water use efficiency as a desired trait for selection in path analysis, a maximum direct effect was observed by grain productivity under the control (0.68) and under drought treatment (0.88). Hom Pathum and Pathum Thani&minus;1 were identified as highly tolerant cultivars in the hierarchical clustering and principal component analysis. It was concluded that the results obtained for the assessment of drought stress on grain productivity, water use efficiency and genotypic variability among these cultivars could be utilized in selection program for stress tolerance and the stress tolerant cultivars could be used for sustaining grain productivity to reduce the impacts of climate change

Assessment of CSM&ndash;CERES&ndash;Rice as a Decision Support Tool in the Identification of High-Yielding Drought-Tolerant Upland Rice Genotypes

Drought is considered as one of the critical abiotic stresses affecting the growth and productivity of upland rice. Advanced and rapid identification of drought-tolerant high-yielding genotypes in comparison to conventional rice breeding trials and assessments can play a decisive role in tackling climate-change-associated drought events. This study has endeavored to explore the potential of the CERES&ndash;Rice model as a decision support tool (DST) in the identification of drought-tolerant high-yielding upland rice genotypes. Two experiments mentioned as potential experiment (1) for model calibration under optimum conditions and an experiment for yield assessment (2) with three irrigation treatments, (i) a control (100% field capacity [FC]), (ii) moderate stress (70% FC), and (iii) severe stress (50 % FC), were conducted. The results from the yield assessment experiment indicated that the grain yield of the studied genotypes decreased by 24&ndash;62% under moderate stress and by 43&ndash;78% under severe stress as compared to the control. The values for the drought susceptibility index (DSI) ranged 0.54&ndash;1.38 for moderate stress and 0.68&ndash;1.23 for severe stress treatment. Based on the DSI and relative yield, genotypes Khao/Sai, Dawk Kham, Dawk Pa&ndash;yawm, Goo Meuang Luang, and Mai Tahk under moderate stress and Dawk Kha, Khao/Sai, Nual Hawm, Dawk Pa&ndash;yawm, and Bow Leb Nahag under severe stress were among the top five drought-tolerant genotypes as well as high-yielding genotypes. The model accurately simulated grain yield under different irrigation treatments with normalized root mean square error &lt; 10%. An inverse relationship between simulated drought stress indices and grain yield was observed in the regression analysis. Simulated stress indices and water use efficiency (WUE) under different irrigation treatments revealed that the identified drought-tolerant high-yielding genotypes had lower values for stress indices and an increasing trend in their WUE indicating that the model was able to aid in decision support for identifying drought-tolerant genotypes. Simulating the drought stress indices could assist in predicting the response of a genotype under drought stress and the final yield at harvest. The results support the idea that the model could be used as a DST in the identification of drought-tolerant high-yielding genotypes in stressed as well as non-stressed conditions, thus assisting in the genotypic selection process in rice crop breeding programs