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	Provedor de dados Thai Agricultural (4) Autor Aran Patanothai (1) Arthit Srikaew (1) Banyong Toomsan (1) Bas Bouman (1) Hatsachai Boonjung (1) Nimitr Vorasoot (1) Nuntawoot Jongrungklang (1) Patcharin Songsri (1) Sakda Jongkaewwattana (1) Sanun Jogloy (1) Somchai Boonpradub (1) Supatsorn Kumbor (1) Surajit Phuphak (1) Mais... Palavra-chave แบบจำลองการเจริญเติบโต (4) ความแม่นยำ (2) ผลผลิต (2) Artificial neural network (1) CSM-CROPGRO-Peanut model (1) Coefficients (1) Crop growth simulation model (1) GIS (1) Growth model (1) Growth models (1) KDML 105 (1) Mungbean (1) ORYZA2000 (1) Peanut (1) Rainfed rice production (1) Risk analysis (1) Root dry weight (1) Root length density (1) Statistical agreement (1) Total dry matter (1) Mais... Tipo do documento Collection (3) Ano 2014 (1) 2011 (1) 2008 (1) 2001 (1) País Thailand (4) Idioma Thailandês (4)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 4 Primeira ... 1 ... Última การปรับค่าสัมประสิทธิ์เพื่อปรับปรุงความแม่นยำของแบบจำลอง CSM-CROPGRO-Peanut ในการเจริญเติบโตของรากถั่วลิสง Thai Agricultural Nuntawoot Jongrungklang; Banyong Toomsan; Sanun Jogloy; Nimitr Vorasoot; Patcharin Songsri; Aran Patanothai. Important input factors that affect root growth under field conditions have not been included in CSM-CROPGRO-Peanut model, resulting in poor simulation of some rooting traits. Hence, the objective of this study was to optimize root growth factors for obtaining the suitable value with particular genotypes and environments. Two root growth factors including 1) fraction partitioning to leaf (YLEAF) and 2) root growth factor in soil layers were adjusted. Root mean square error (RMSE) and index of agreement (d) that were the statistical agreement between observed data and simulated data were used for considering the best fit values of root growth factor. The statistical agreement between observed and simulated data of root dry weight and root length density... Tipo: Collection Palavras-chave: Peanut; Root dry weight; Root length density; Statistical agreement; Growth model; Coefficients; Total dry matter; CSM-CROPGRO-Peanut model; ถั่วลิสง; น้ำหนักแห้งราก; ความหนาแน่นราก; ความสอดคล้องทางสถิต; แบบจำลองการเจริญเติบโต; น้ำหนักแห้งทั้งหมด; ค่าสัมประสิทธิ์; ความแม่นยำ; พันธุ์; สภาพแวดล้อม. Ano: 2011 URL: http://anchan.lib.ku.ac.th/agnet/handle/001/5114 การประเมินค่าสัมประสิทธิ์พันธุกรรมข้าวโพดและการทดสอบความแม่นยำของแบบจำลองการเจริญเติบโตของข้าวโพด Thai Agricultural Somchai Boonpradub; Sakda Jongkaewwattana. 2 ill., 2 tables Palavras-chave: ข้าวโพด; สัมประสิทธิ์พันธุกรรม; แบบจำลองการเจริญเติบโต; ความแม่นยำ. Ano: 2001 URL: http://anchan.lib.ku.ac.th/agnet/handle/001/4173 การวิเคราะห์ความเสี่ยงในการปลูกข้าวนาน้ำฝนโดยใช้ระบบสารสนเทศภูมิศาสตร์และแบบจำลองการเจริญเติบโตของพืช Thai Agricultural Surajit Phuphak; Bas Bouman. Risk due to rainfall variability is crucial in rainfed rice growing areas in the NE Thailand therefore analysis on risk coping of the farmers is importance. This research had evaluated the yield of KDML105 rice variety using a knowledge-based system approach, subject to rainfall uncertainty in 6 different kinds of paddies that related to soil and hydrological variability, with 4 scenarios of sowing times and 2 sowing methods in a watershed area of Ubon Ratchathani province. A GIS and a rice growth simulation model, ORYZA2000, tools were used with stochastic efficiency rule to analyze risk. The results showed that KDML105 yield was sensitive to rainfall variability, sowing times and sowing methods rather than variability of soil and hydrology which related... Tipo: Collection Palavras-chave: Risk analysis; Rainfed rice production; Crop growth simulation model; GIS; ORYZA2000; KDML 105; Yield; ข้าว; การปลูกข้าวนาน้ำฝน; แบบจำลองการเจริญเติบโต; จีไอเอส; ORYZA2000; พันธุ์ขาวดอกมะลิ 105; ผลผลิต; การวิเคราะห์ความเสี่ยง. Ano: 2008 URL: http://anchan.lib.ku.ac.th/agnet/handle/001/5102 แบบจำลองการเจริญเติบโตและผลผลิตของถั่วเขียว โดยใช้โครงข่ายประสาทเทียม Thai Agricultural Supatsorn Kumbor; Hatsachai Boonjung; Arthit Srikaew. Most of crop modeling is mechanistic model whereas the objective of this study was to predict mungbean growth and yield by artificial neural network (ANN). The experiment was 2 varieties (SUT1 and KPS2) x 2 water levels (rainfed and irrigation) x 3 fertilizer levels (12-24-12 rate 0, 15 and 30kg./rai) factorial experiment in RCBD 4 blocks growing for 2 seasons (no rainfed in the 2nd season). Data was collected for 10 replications in each plot. Using 560 sets of data from fertilizer rate of 0 and 30 kg/rai trained the ANN model by back propagation algorithm. The input variables were variety, fertilizer, irrigations, seasonal, growing degree day, rainfall, solar radiation and day after planting. The rest of 280 sets of data (only 15 kg/rai of fertilizer)... Tipo: Collection Palavras-chave: Mungbean; Artificial neural network; Growth models; Yield models; ถั่วเขียว; โครงข่ายประสาทเทียม; แบบจำลองการเจริญเติบโต; ผลผลิต. Ano: 2014 URL: http://anchan.lib.ku.ac.th/agnet/handle/001/5570 Registros recuperados: 4 Primeira ... 1 ... Última

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