HANOI UNIVERSITY OF AGRICULTUREFACULTY OF AGRONOMY TITLE: “Comparison of the paddy rice yield models responding nitrogen in Vietnam” Student: Pham Van Chuyen Class: KHCT52T Year: 2011 Ma
Trang 1HANOI UNIVERSITY OF AGRICULTURE
FACULTY OF AGRONOMY
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
Trang 2CHAPTER 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,
Trang 3fertilizer, 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
Trang 4CHAPTER 2 - LITERATURE REVIEW
Nitrogen is the most important nutrient for rice and affect the yield an quality of
seed[ CITATION SYo81 \l 1033 ] 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[ CITATION MEC90 \l 1033 ] 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[ CITATION Oveman02 \l 1033 ] 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
Trang 5Experiment 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 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]
Trang 6Where 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]
Trang 7Y = 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
Y = A
[1+exp (b−cN )] [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:
ln(Y A−1)=b−cX [8]
And [8] equation is linear with independent variance X and dependent variance
ln(Y A−1).It is analyzed though linear regression by SPSS v.16 of IPM Corporation
Trang 83.2.4 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
X2
=∑( observed Y −expected Y )
2
expected Y
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
X2
3, 0.1= 6.251
X2
3, 0.05= 7.815
X2
3, 0.01= 11.345
Comparing the calculated X2 with critical value is to evaluate the probability level
CHAPTER 5 - CONCLUSION AND RECOMMENDATION
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