Sri Paddy Growth and GHG Emissions at Various Groundwater Levels

Setiawan, Budi Indra; Imansyah, Arief; Arif, Chusnul; Watanabe, Teiji; Mizoguchi, Masahiro; Kato, Hisaaki

doi:10.1002/ird.1866

Cited by 18 publications

(15 citation statements)

References 27 publications

(27 reference statements)

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“…Their ANN model had reasonable predictive ability, with correlation coefficients of 0.72 and 0.70 for CH 4 and N 2 O respectively. The same group of authors [16] then extended and refined their work, obtaining a better correlation between ANN predictions and actual data by substituting the groundwater level (GWL) for the VMC in the input parameters.…”

Section: The Prior Artmentioning

confidence: 99%

Modelling Methane and Nitrous Oxide Emissions from Rice Paddy Wetlands in India Using Artificial Neural Networks (ANNs)

Abbasi

Luithui

et al. 2019

Water

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Paddy fields, which are shallow man-made wetlands, are estimated to be responsible for ~11% of the total methane emissions attributed to anthropogenic sources. The role of water use in driving these emissions, and the apportioning of the emissions to individual countries engaged in paddy cultivation, are aspects that have been mired in controversy and disagreement. This is largely due to the fact that methane (CH4) emissions not only change with the cultivar type but also regions, climate, soil type, soil conditions, manner of irrigation, type and quantity of fertilizer added—to name a few. The factors which can influence these aspects also encompass a wide range, and have origins in causes which can be physical, chemical, biological, and combinations of these. Exceedingly complex feedback mechanisms, exerting different magnitudes and types of influences on CH4 emissions under different conditions, are operative. Similar is the case of nitrous oxide (N2O); indeed, the present level of understanding of the factors which influence the quantum of its emission is still more patchy. This makes it difficult to even understand precisely the role of the myriad factors, less so model them. The challenge is made even more daunting by the fact that accurate and precise data on most of these aspects is lacking. This makes it nearly impossible to develop analytical models linking causes with effects vis a vis CH4 and N2O emissions from paddy fields. For situations like this the bioinspired artificial intelligence technique of artificial neural network (ANN), which can model a phenomenon on the basis of past data and without the explicit understanding of the mechanism phenomena, may prove useful. However, no such model for CH4 or N2O has been developed so far. Hence the present work was undertaken. It describes ANN-based models developed by us to predict CH4 and N2O emissions using soil characteristics, fertilizer inputs, and rice cultivar yield as inputs. Upon testing the predictive ability of the models with sets of data not used in model development, it was seen that there was excellent agreement between model forecasts and experimental findings, leading to correlations coefficients of 0.991 and 0.96, and root mean square error (RMSE) of 11.17 and 261.3, respectively, for CH4 and N2O emissions. Thus, the models can be used to estimate CH4 and N2O emissions from all those continuously flooded paddy wetlands for which data on total organic carbon, soil electrical conductivity, applied nitrogen, phosphorous and potassium, NPK, and grain yield is available.

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Section: The Prior Artmentioning

confidence: 99%

Modelling Methane and Nitrous Oxide Emissions from Rice Paddy Wetlands in India Using Artificial Neural Networks (ANNs)

Abbasi

Luithui

et al. 2019

Water

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“…For SRI application in the field, it is recommended to keep soil in wet condition but not submerged by draining water shortly after the transplanting, i.e., during the vegetative stage, and then maintaining thin water layer after panicle formation as conducted in this study (Uphoff, Kassam, & Harwood, 2011). It is also recommended to maintain the water level close to the soil's surface as best the water management practice for SRI in term of producing more yield and having a minimum negative environmental impact in terms of greenhouse gas emission (Setiawan et al, 2014).…”

Section: Hydrological Conditionsmentioning

confidence: 99%

Water balance analysis on water management of organic System of Rice Intensification (organic-SRI) in West Java, Indonesia

Arif

Setiawan

Saputra

et al. 2019

Self Cite

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ABSTRAKPenelitian ini dilakukan untuk menganalisa efektifitas irigasi berselang dengan neraca air pada lahan SRI dengan penerapan pupuk organik. Eksperimen dilakukan di Desa Gabus Wetan, Kab. Indramayu, Jawa Barat dari 17 November 2016 sampai 1 Maret 2017. Sensor parameter cuaca dan tanah dipasang di lahan untuk mendapatkan data harian kondisi lapang termasuk pertumbuhan tanaman. Data cuaca seperti hujan, suhu udara, kelembaban udara relatif, dan kecepatan angin dan kedalaman muka air diukur secara otomatis setiap 60 menit. Analisis neraca air dilakukan dengan kesalahan (error) yang rendah (1,00%) dimana jumlah air masuk melalui hujan dan irigasi sebesar 560 mm dan 865 mm, sedangkan jumlah air keluar melalui evapotranspirasi tanaman, perkolasi, dan limpasan berturut turut sebanyak 430, 306 dan 675 mm. Perbandingan dengan sistem pertanian konvensional dengan irigasi tergenang, menunjukkan bahwa produktivitas air dari SRI organik berturut turut 30% dan 27% lebih tinggi untuk produktivitas air berdasarkan jumlah air masuk dan evapotranspirasi. SRI organik juga memproduksi 33% produksi lebih tinggi dari sistem pertanian konvensional di lokasi yang sama. Kunci keberhasilan irigasi berselang adalah dengan menjaga tinggi muka air dipermukaan tanah (macak-macak) pada fase vegetatif dan generatif. Oleh sebab itu, cara ini merupakan alternatif pilihan bagi petani ketika sumber daya air berkurang karena perubahan iklim. Diseminasi hasil direkomendasikan melalui program pelatihan dan pendampingan bagi petani.Kata Kunci: SRI organik, pengelolaan air, neraca air, produktivitas, sistem monitoring ABSTRACTThe current study was carried out to analyze effectiveness of intermittent irrigation by water balance components in SRI paddy fields with organic fertilizer. The experiment was conducted in Gabus Wetan Village, Indramayu District, West Java, Indonesia from 17 November 2016 to 1 March 2017. Weather and soil sensors were set up in the fields to acquire data on daily field conditions as well as on plant growth. Data on weathers such as precipitation, air temperature, relative humidity, and wind speed were collected automatically during the season every 60 min, as well as the soil water depth. Analyzing the data collected, water balance analysis was well performed with low error (1.00%) in which the water inflows through precipitation and irrigation were 560 mm and 865 mm, respectively, while the outflows by crop evapotranspiration, percolation, and runoff were 430, 306 and 675 mm, respectively. Compared to conventional rice farming as commonly practices by applying continous flooding irrigation, water productivities of organic SRI were 30% and 27% higher with respect to total water input (WPIR) and with respect to the amount of evapotranspiration (WPET, respectively. Organic SRI produced 33% higher yield than the average of conventional methods in the same subdistrict. The key was intermittent irrigation by maintaining shallow water depth (nearly soil surface) in the vegetative and generative stages. Therefore, it is an attractiv...

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“…In the present study, nine different scenarios were developed from the combination of drainage outlet height and minimum ponding depth to evaluate the effects of water control on reduced nutrient loads (Table V). The drainage outlet heights (80, 100 and 120 mm) and minimum ponding depths (20, 40 and 60 mm) were selected based on previous studies that related water control methods with rice production (Mishra et al, 1998;Son and Chung, 2002;Zeng et al, 2003;Setiawan et al, 2014). Son and Chung (2002) reported that three minimum ponding depths (20, 40 and 100 mm) did not show any statistical differences in growth and rice yield.…”

Section: Scenario Analysismentioning

confidence: 99%

Paddy Field Modelling System For Water Quality Management

Song

Ryu

Park

et al. 2016

Irrigation and Drainage

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The objective of this study was to develop a paddy field modelling system for predicting pollutant (nutrient) loads and evaluating various best management practices (BMPs) on paddy fields. The system consists of a pre‐processing component, the CREAMS‐PADDY (Chemicals, Runoff, and Erosion from Agricultural Management Systems for PADDY) model, and a post‐processing component. The CREAMS‐PADDY was selected as the core model. The pre‐processing component included manipulation of meteorological data, management of input parameters such as spatial characteristics and farming methods, batch running, and implementing various scenarios. The post‐processing component contained visualization of water and nutrient balance components and statistical analyses for calibration, validation and application. To evaluate the applicability of the developed system, reduction effects on water control methods in paddy fields were simulated. Model parameters related to hydrology and water quality were calibrated and validated by comparing model predictions with field data collected over 2 years. The simulation results indicated that reducing nutrient loss through surface drainage by water‐saving irrigation and raising drainage outlet heights could protect downstream water quality. The simulation system will be a useful tool for planning BMPs to reduce nutrient loads from paddy fields in various regions including Korea. Copyright © 2016 John Wiley & Sons, Ltd.

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Sri Paddy Growth and GHG Emissions at Various Groundwater Levels

Cited by 18 publications

References 27 publications

Modelling Methane and Nitrous Oxide Emissions from Rice Paddy Wetlands in India Using Artificial Neural Networks (ANNs)

Modelling Methane and Nitrous Oxide Emissions from Rice Paddy Wetlands in India Using Artificial Neural Networks (ANNs)

Water balance analysis on water management of organic System of Rice Intensification (organic-SRI) in West Java, Indonesia

Paddy Field Modelling System For Water Quality Management

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