HANOI UNIVERSITY OF AGRICULTURE FACULTY OF AGRONOMY UNDERGRADUATE THESIS PROPOSAl TITLE: “Comparison of the paddy rice yield models responding nitrogen in Vietnam” Student: Pham Van Chuyen Class: KHCT52T Year: 2011 Major: Crop science Supervisor: Dr. Vu Duy Binh Department: Faculty of Information Technology Hanoi - 2011 CHAPTER 1 - GENERAL INTRODUCTION 1.1 Introduction Paddy rice is the most important crop of 5 main cereals in Vietnam. Not only ensuring food security in Vietnam, rice production is as source of foreign currency earnings of country. In 2009, rice production was grown in 7.4 million hectare area and obtained 38.9 grain million tones. Exporting rice quantity was about 5.6 million tones, responsed 2.8 billion USD. While rice area hasn’t increased and trends to decrease, increasing the rice productivity is essentially to ensure the food security and exporting. To do it, rice production technology need to be provided as: selecting the high yield varieties, irrigation management, fertilizers, pest management, tillage In there, mostly the applied fertilizer quantities are derived from field studies in which crop yield and quality responses to a range of fertilizer rates are measured. Responses are often modeled to determine optimum fertilizer rate. The lacks of these studies are economic efficiency and environment pollution of over-fertilization. So, the fertilizer response yield mathematic models applied to improve these lacks. Some study shows nitrogen is the most important fertilizer factor that determines the rice yield and is very interested in rice simulation models. Proportion between applied nitrogen and yield are positive and depends on rice varieties, time, by soil type, by cultural practice and by weather. It is very complex. Simulation ability of the nitrogen response yield models is not complete accuracy and so, depends on their functions. Rice simulation models are developed from 1970s and are full-made gradually with ORYZA2000, CERES-RICE, DSSAT, and APSIM These models integrated of many modules that simulate all three different production situations (potential, fertilizer, weather, soil, culture…). For the same module in the simulation model, response can be appeared by difference mathematic models. Today, for nitrogen response yield models, they usually have the forms: response curves were linear (L), quadratic (Q), and linear with plateau (LP), quadratic with plateau (QP), logistic (LO) functions. The good fit of the above equations to the observed data appears at differently level. It is essential to evaluate the nitrogen response yield models for paddy rice in Vietnam. Purpose of the study is to compare the performance of the yield models for paddy rice responding nitrogen in Vietnam and select the model having the best performance. The study named “comparison of the paddy rice yield models responding nitrogen in Vietnam” 1.2 Objectives and requirements 1.2.1 Objectives To evaluate the good fit of the nitrogen response yield models for paddy rice varieties in Vietnam. To encourage the use of the best-performing model in the rice-nitrogen studies To help in fertilization recommendations that result in optimum rice yield and quality without risking over fertilization. 1.2.2 Requirements Determine values and confident of the coefficients in the functions and interpret their mean Compare the predicted yield and the observed yield by Chi-square test Evaluate affecting of their rice varieties for the minimum and maximum yield. CHAPTER 2 - LITERATURE REVIEW Nitrogen is the most important nutrient for rice and affect the yield an quality of seed (Yoshida S, 1981). Rice cans uptake nitrogen from soil, applied fertilizer, organic debris. When increasing the nitrogen fertilizer, yield can significantly. Fertilizer introduction trends to optimize the rice yield. This usually causes the environment pollution due to over-fertilizer and currently reduces the economic profit. Application of mathematic model can solve the above problem. Linear (L), quadratic (Q), and linear with plateau (LP), quadratic with plateau (QP) functions are introduced to simulate the rice yield response fertilizer (Blackmer, 1990). Linear model is proposed early. It is simply, easy to use. it describe the linearly proportion between applied nitrogen and yield. In fact, with high nitrogen, response yield doesn’t increase. The model is not significance at high nitrogen To improve the limitation of the linear model, some study use combination of linear and plateau equations to fit data. Logistic model expressed the goodness of fit to foliage, vegetable crop response nitrogen (Overman, A.R. and Scholtz, R.V. III, 2002). For cereal as maize, wheat, barley, the application of logistic model in yield simulation responding nitrogen also showed the good fit. CHAPTER 3 - MATERIALS AND METHODS 3.1. Experimental site and duration 3.1.1 Experimental site Experiment is conduct in the field at Faculty of Agronomy, Hanoi university of Agriculture, Hanoi 3.1.2 Duration Summer-autumn season in 2004 3.2 Methods 3.2.1 Materials Materials consist of 3 rice varieties including 2 hybrids Vietlai 20, Bac Uu 903, 1 inbreeding CRD There are 4 applied nitrogen fertilizer rates: 0, 60, 120, 180 kg N/ha. With each dose of nitrogen were accompanied by an common dose 90 kg P 2 O 5 /ha and 60 kg K 2 0/ha The total of 4 fertilizer rate for 3 rice varieties (Vietlai 20, Bac Uu 903, CRD) result to 12 experiment units. The experimental units were arranged in a random block design with 3 replications and unit area is 15 m 2 . The grain yield was measured at 14% moisture content 3.2.2 Yield model 3.2.2.1 Linear (L) model The L function model is defined by the following equation. Y = Y 0 + bX [1] Where Y = grain yield (tons/ha), X = fertilizer application rate (kg/ha), Y 0 = grain yield at 0 nitrogen kg/ha, b = applied N coefficient for rice yield (tons/kg). Constant b is obtained by fitting data to the model function 3.2.2.2 Quadratic (Q) model The Q model is defined by the equation Y = a + bX + cX2 [2] Where Y is grain yield (tons/ha), X is fertilizer application rate (kg/ha), and a (Intercept), b (linear coefficient), and c (quadratic coefficient) are constants obtained by fitting data to the model function. 3.2.2.3 Linear with plateau (LP) model (LP) model is described following Y = a + bX if X < C [3] Y = P if X ≥ C [4] Where Y = grain yield (tons/ha), X = fertilizer application rate (kg/ha), a = intercept parameter, b = applied N coefficient for rice yield (tons/kg), C =critical fertilizer rate (kg/ha), which occurs at the intersection of the linear response and the plateau lines), and P (plateau yield) is the constant obtained by fitting data to the model function. 3.2.2.3 Quadratic with plateau (QP) model The QP model is defined by following equations: Y = a + bX + cX2 if X < C [5] Y = P if X ≥ C [6] Where Y is grain yield (tons/ha), X is fertilizer application rate (kg/ha), and a (intercept), b (linear coefficient), and c (quadratic coefficient), C (critical fertilizer rate, which occurs at the intersection of the quadratic response and the plateau lines), and P (plateau yield) is the constant obtained by fitting data to the model function 3.2.2.4 Logistic (LO) model Logistic model was defined by following equation [7] Where Y=grain yield, N= applied nitrogen rate (kg/ha), A= maximum grain yield at high nitrogen, b= intercept parameter, c= nitrogen response coefficient. 3.2.3 Analysis of variances (ANOVA) For (1), (4), (6) equations, the plateau yield are defined as maximum yield at high nitrogen and are estimate by virtual inspection. The analysis of variance of the linear and quadratic equation are performed though linear and quadratic regression respectively by SPSS v.16 of IPM Corporation For the logistic model, the [7] equation is linearized to the form: [8] And [8] equation is linear with independent variance X and dependent variance It is analyzed though linear regression by SPSS v.16 of IPM Corporation 3.2.4 Chi-square Test Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to the equations Number of applied nitrogen fertilizer is 4 Degrees of Freedom is df=n-1=4-1=3 Critical values of X 2 at degree of freedom df=3 are respectively 90%, 95%, 99% probability levels X 2 3, 0.1 = 6.251 X 2 3, 0.05 = 7.815 X 2 3, 0.01 = 11.345 Comparing the calculated X 2 with critical value is to evaluate the probability level. CHAPTER 5 - CONCLUSION AND RECOMMENDATION CHAPTER 6 – REFERENCES A. Dobermann, T.H. Fairhurst. (2000). Nutrient Disorders & Nutrient Management. Singapo: Potash & Phosphate Institute (PPI), Potash & Phosphate Institute of Canada (PPIC) and International Rice Research Institute (IRRI). Blackmer, M. E. (1990). Comparison of Models for Describing; Corn Yield Response to Nitrogen Fertilizer. Agron. J. 82:138-143. Bouman BAM, Kropff MJ, Tuong TP, Wopereis MCS, ten Berge HFM, van Laar HH. (2001). ORYZA 2000: modeling lowland rice. Mainila (Philipin): International Rice Research Institute. Dũng, N. T. (2007). kết quả ứng dụng hệ thống canh tác lúa (system of rice intensification-sri) ở các vùng sinh thái phía bắc (2005-2006). Cục Bảo vệ thực vật . Frans R. Moormann and Nico van Breemen. (1978). Rice: Soil, Water, Land. Manila , Philippines: International Rice Research Institute. Garcia, A.G. y; Dourado-Neto, D.; Basanta, M. del V.; Ovejero, R.F.L.; Favarin, J.L. (2003). Logistic rice model for dry matter and nutrient uptake (Vol. v. 60(3) ). Scientia Agrícola. Glantz, S. A. (2002). Primer of Biostatistics, 5th Edition. McGraw-Hill. IRRI. (1987). Efficiency of nitrogen fertilizers for rice. Manila, Philipin: International Rice Research Institute . Kirkham. (2005). Principles of Soil and Plant Water Relations. Elsevier, Amsterdam. Mahdi, S. P. (2008). A Comparison of Three Mathematical Models of response to Applied Nitrogen Using Spinach. Varamin: IDOSI Publications. Mamaril, C. P. (1984). Increasing the efficiency of nitrogen fertilizer on rice. Overman, A.R. and Scholtz, R.V. III. (2002). Mathematical Models of Crop Growth and Yield. New York: Marcel Dekker AG. Pham Van Cuong, Pham Thi Khuyen, and Pham thi Dieu. (2004). Affection of Nitrogen fertilizer level on dry matter production at different growth stage and grain yield of several F hybrid and inbred rice cultivars. Hanoi, Vietnam: Hanoi university of Agricultural. Priyadarshani, S. H. Renuka. the effect of plant nutrition on crop growth and water balance elements in rainfed lowland rice (Oryza sativa L.). Los Baños, Laguna, Philippines. S.M. Haefele, K. Naklang, D. Harnpichitvitaya, S. Jearakongman, E. Skulkhu, P. Romyen, S. Phasopa, S. Tabtim, D. Suriya-arunroj, S. Khunthasuvon, D. Kraisorakul, P. Youngsuk, S.T. Amarante and L.J. Wade. (2005). Factors affecting rice yield and fertilizer response in rainfed lowlands of northeast Thailand. Field Crops Research 98 (2006) 39–51. Trường Cao đẳng Tài nguyên và Môi trường Tp.HCM. (2010). Đất trồng lúa nước. Tuong TP, Kam SP, Wade L, Pandey S, Bouman BAM, Hardy B. (2000). Characterizing and understanding rainfed environments. Proceedings of the International Workshop on Characterizing and Understanding Rainfed enviroment. Bali, Indonesia., Los Baños (Philippines): International Rice Research Institute. V. M. Chowdary, N. H. Rao and P. B. S. Sarma. A coupled soil water and nitrogen balance model for flooded rice fields in India (Vol. 103). Agriculture, Ecosystems & Environment. Yang J., Greenwood D.J., Rowell D.L., Wadsworth G.A., Burns I.G. (2000). Statistical methods for evaluating a crop nitrogen simulation model, N_ABLE. Agricultural Systems . . Mainila (Philipin): International Rice Research Institute. Dũng, N. T. (2007). kết quả ứng dụng hệ thống canh tác lúa (system of rice intensification-sri) ở các vùng sinh thái phía bắc (2005-2006) Thailand. Field Crops Research 98 (2006) 39–51. Trường Cao đẳng Tài nguyên và Môi trường Tp.HCM. (2010). Đất trồng lúa nước. Tuong TP, Kam SP, Wade L, Pandey S, Bouman BAM, Hardy B. (2000).