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HANOI UNIVERSITY OF AGRICULTURE
FACULTY OFAGRONOMY
UNDERGRADUATETHESIS PROPOSAl
TITLE: “Comparison of the paddy rice yield models responding nitrogenin 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. 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.
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. 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 ofAgronomy, 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 P2O5 /ha and 60 kg K20/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 m2.
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 = Y0 + bX [1]
Where Y = grain yield (tons/ha), X = fertilizer application rate (kg/ha), Y0 = 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]
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