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AGRICULTURAL SCIENCE

Edited by Godwin Aflakpui

Agricultural Science

Edited by Godwin Aflakpui

Published by InTech

Janeza Trdine 9, 51000 Rijeka, Croatia

Copyright © 2012 InTech

All chapters are Open Access distributed under the Creative Commons Attribution 3.0

license, which allows users to download, copy and build upon published articles even for

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Notice

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no

responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

Publishing Process Manager Jana Sertic

Technical Editor Teodora Smiljanic

Cover Designer InTech Design Team

First published April, 2012

Printed in Croatia

A free online edition of this book is available at www.intechopen.com

Additional hard copies can be obtained from orders@intechopen.com

Agricultural Science, Edited by Godwin Aflakpui

p. cm.

ISBN 978-953-51-0567-1

Contents

Preface IX

Section 1

Crop Improvement 1

Chapter 1

Impact of Epistasis in

Inheritance of Quantitative Traits in Crops 3

Bnejdi Fethi and El Gazzeh Mohamed

Chapter 2

Genetic Diversity Analysis of

Heliconia psittacorum Cultivars and Interspecific

Hybrids Using Nuclear and Chloroplast DNA Regions 11

Walma Nogueira Ramos Guimarães,

Gabriela de Morais Guerra Ferraz,

Luiza Suely Semen Martins, Luciane Vilela Resende,

Helio Almeida Burity and Vivian Loges

Section 2

Crop Production 23

Chapter 3

Concepts in Crop Rotations 25

H. Arnold Bruns

Chapter 4

Texture, Color and Frequential Proxy-Detection

Image Processing for Crop Characterization in a

Context of Precision Agriculture 49

Cointault Frédéric, Journaux Ludovic, Rabatel Gilles,

Germain Christian, Ooms David, Destain Marie-France,

Gorretta Nathalie, Grenier Gilbert, Lavialle Olivier and

Marin Ambroise

Section 3

Crop Response to Water and Nutrients 71

Chapter 5

Spatial Patterns of Water and

Nitrogen Response Within Corn Production Fields 73

Jerry L. Hatfield

Chapter 6

Long-Term Mineral Fertilization and Soil Fertility 97

Margarita Nankova

VI

Contents

Chapter 7

Effect of Mixed Amino Acids on Crop Growth 119

Xing-Quan Liu and Kyu-Seung Lee

Section 4

Crop Response to Temperature 159

Chapter 8

Plant Temperature for Sterile Alteration of Rice 161

Chuan-Gen Lű

Section 5

Crop Protection 183

Chapter 9

Infrared Spectroscopy Applied to

Identification and Detection of Microorganisms and

Their Metabolites on Cereals (Corn, Wheat, and Barley) 185

Cécile Levasseur-Garcia

Chapter 10

Insect Pests of Green Gram

Vigna radiata (L.) Wilczek and Their Management 197

R. Swaminathan, Kan Singh and V. Nepalia

Section 6

Agriculture and Human Health 223

Chapter 11

The Agricultural Landscape for Recreation 225

Erik Skärbäck, John Wadbro, Jonas Björk,

Kim de Jong, Maria Albin, Jonas Ardö and Patrik Grahn

Section 7

Animal Nutrition 243

Chapter 12

Performance and Heat Index of

West African Dwarf (WAD) Rams Fed with

Adansonia digitata Bark (Baobab) as Supplement 245

Idayat Odunola Agboola

Preface

The whole world needs agriculture because agriculture does not only feed the entire

human race but also produces fibre for clothing, feed for livestock and bio-energy. In

the developing world agriculture contributes significantly to the gross domestic

product, leads the way in reducing poverty and accounts for the lion’s share of

employment opportunities especially for women. Agriculture also has one of the

highest potentials for reducing carbon emissions and helping vulnerable people adapt

to climate change.

The Food and Agriculture Organisation (FAO) of the United Nations and the World

Bank have indicated that:

100% of the global population depends on agriculture for nutrition

40% of the global population relies on agriculture for employment

70% of worldwide withdrawal of water is for the purposes of agriculture

30% proportion of greenhouse gas emissions is related to agriculture

70% increment in food production is needed to feed a global population of 9

billion by 2050.

These figures by the FAO and the World Bank indicate that without agriculture, the

world economy would not be what it has been and what it is today.

For agriculture to continuously contribute to food security, environmental

sustainability and economic opportunities by driving the rural and national economic

development with well targeted investments, it is imperative that the research and

development agenda which is based on the science behind agriculture must be

pursued vigorously. It is in this context that this book, Agricultural Science has been

written with multiple authors compiling some important state-of-the art contributions

on the subject in recent years.

The contributions of chapters in the book are divided into seven sections: Crop

Improvement, Crop Production, Crop Response to Water and Nutrients, Crop

Response to Temperature, Crop Protection, Agriculture and Human Health, and

Animal Nutrition. The sections vary in the number of chapters which was largely due

to the number of authors who contributed chapters to publish the book. The chapters

in each section and in the book in general vary in scope and the way they attempt to

X

Preface

manipulate resources and variables to improve on productivity and also to link

agricultural landscape to recreation and therefore human health, albeit remotely.

I acknowledge the authors for willingly contributing their chapters without which we

could not have published this book. I am equally grateful to Ms Jana Sertic, the

Publishing Process Manager for the able assistance she provided and to the

Information Technology Department for providing the requisite framework that

greatly enhanced the work of putting together the chapters in the book. The Technical

Editors deserve commendation for preparing the online publication and print versions

of the book.

Finally, I owe a debt of gratitude to the Scientific Board of the INTECH OPEN

ACCESS PUBLISHER for the trust reposed in me to edit this book. I am most grateful

to be of service to the scientific community.

Dr. Godwin Aflakpui

Rector, Wa Polytechnic, Wa, Upper West Region,

Ghana, West Africa

Section 1

Crop Improvement

1

Impact of Epistasis in Inheritance of

Quantitative Traits in Crops

Bnejdi Fethi and El Gazzeh Mohamed

Laboratoire de Génétique et Biométrie Faculté des Sciences de Tunis,

Université Tunis, El Manar,

Tunisia

1. Introduction

Epistasis is the interaction between alleles of different genes, i.e. non-allelic interaction, as opposed to dominance, which is interaction between allele of the same gene, called inter-allelic or intra-genic interaction (Kearsey and Pooni, 1996). Statistical epistasis describes the deviation that occurs when the combined additive effect of two or more genes does not

explain an observed phenotype (Falconer and Mackay, 1996).

The heritability of a trait, an essential concept in genetics quantitative, “certainly one of the central points in plant breeding research is the proportion of variation among individuals in a population that” is due to variation in the additive genetic (i.e., breeding) values of individuals:

h2 = VA/VP = Variance of breeding values/ phenotypic variance (Lynch and Walsh, 1998).

This definition is now termed “heritability in the narrow-sense” (Nyquist, 1991). Estimation of this parameter was prerequisite for the amelioration of quantitative traits. As well as choosing the selective procedure, that will maximize genetic gain with one or more selection cycles.

Various methods were developed in the past, Warner (1952), Sib-Analysis, Parent-offspring regressions etc. Theses methods considered that additive-dominant model is fitted, assuming epistasis to be negligible or non existent. Because of the complexity of theoretical genetics studies on epistasis, there is a lack of information about the contribution of the epistatic components of genotypic variance when predicting gains from selection. The estimation of

epistatic components of genotypic variance is unusual in genetic studies because the limitation of the methodology, as in the case of the triple test cross, the high number of generations to be produced and assessed (Viana, 2000), and mainly because only one type of progeny, Half-Sib, Full-Sib or inbred families, is commonly included in the experiments (Viana, 2005). If there is no epistasis, generally it is satisfactory to assess the selection efficiency and to predict gain based on the broad-sense heritability. Therefore, the bias in the estimate of the additive variance when assuming the additive-dominant model is considerable. The preponderance of

epistasis effect in the inheritance of quantitative trait in crops was recently reported by many geneticists (Pensuk et al., 2004; Bnejdi and El Gazzah, 2008; Bnejdi et al. 2009; Bnejdi and El-Gazzah, 2010a; Shashikumar et al. 2010). Epistasis can have an important influence on a

number of evolutionary phenomena, including the genetic divergence between species.

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Agricultural Science

The aims of our study were to determine the importance of epistasis effects in heredity of quantitative traits and their consequences in the bias of four methods of estimation of

narrow-sense heritability.

2. Origin of data and genetic model

Nine quantitative traits with 88 cases of combination cross-site, cross-isolate or cross-

treatment of six generations (P1, P2, F1, F2, BC1 and BC2) for three crops ( Triticum Durum, Capsicum annum and Avena sp) were collected from different works realised in our laboratory. Crops, traits and origin of data are reported in Table 1. For each trait parents of crosses were extreme. Transformations (such as Kleckowski transforms (Lynch and Walsh,

1998)) were applied to normalize the distribution of data or to make means independent of variances for several traits.

Durum Wheat

( Triticum durum)

Two crosses/two sites

Number of head per plant , Spiklets per spike and Number of grains per spike (Bnejdi and

El Gazzeh 2010b)

Four crosses/ one site

Resistance to yellowberry (Bnejdi and El Gazzah, 2008)

Four crosses/ one site

Resistance to yellowberry (Bnejdi et al., 2010a)

Four crosses/ Two sites

Grain protein content (Bnejdi and El Gazzeh, 2010a)

Two crosses/ Five salt treatments

Resistance to salt at germination stage (Bnejdi et al., 2011a)

Two crosses/ fifteen isolates

Resistance to Septoria tritici (Bnejdi et al., 2011b)

Pepper

( Capsicum annuum L.)

Two crosses/ Two isolates

Resistance to Phytophthora nicotianae (Bnejdi et al., 2009)

Two crosses/ Six isolates

Resistance to Phytophthora nicotianae (Bnejdi et al., 2010b)

Oates

(Avena sp.)

Two crosses/ Two isolates

Resistance to P. coronate Cda. f. sp. avenae Eriks (Bnejdi et al., 2010c)

Table 1. Traits assessed in each crop and date of publication

Impact of Epistasis in Inheritance of Quantitative Traits in Crops

5

2.1 Best genetic model

Weighted least squares regression analyses were used to solve for mid-parent [M] pooled additive [A], pooled dominance [D] and pooled digenic epistatic ([AA], [DD] and [AD])

genetic effects, following the models and assumptions described in Mather and Jinks (1982).

A simple additive-dominance genetic model containing only M, A and D effects was first tested using the joint scaling test described in Rowe and Alexander (1980). Adequacy of the genetic model was assessed using a chi-square goodness-of-fit statistic derived from

deviations from this model. If statistically significant at P < 0.05, genetic models containing digenic epistatic effects were then tested until the chi-square statistic was non-significant.

3. Phenotypic resemblance between relatives

We now will use the covariance (and the related measures of correlations and regression

slopes) to quantify the phenotypic resemblance between relatives. Quantitative genetics as a field traces back to Fisher’s 1918 paper showing how to use the phenotypic covariance to

estimate genetic variances, whereby the phenotypic covariance between relatives is

expressed in terms of genetic variances, as we detail below.

3.1 Parent-offspring regressions

There are three types of parent-offspring regressions: two single parent - offspring

regressions (plotting offspring mean versus either the trait value in their male parent Pf or their female parent Pm), and the mid-parent-offspring regression (the offspring mean regressed on the mean of their parents, the mid-parent MP = ( Pf + Pm) / 2).

The slope of the (single) parent-offspring regression is estimated by

Co (

v O, P)

1

n

b

, where

(

Cov O, P) 

(  Oi i

P n .

O P)

o/ p

Var( P)

n1 i1

Where Oi is the mean trait value in the offspring of parent i (Pi) and we examine n pairs of parent-offspring. One could compute separate regressions using males ( Pm) and females ( Pf), although the later potentially includes maternal effect contributions and hence single-parent regressions usually restricted to fathers.

Co (

v O, P)

o

b / p

Var( P)

2

2

2

2

A

AA AAA AAAA

Co (

v O, P) 

 (

 .........)

2

4

8

16

2

2

2

2

*

Co (

v O, P)  A

1  AA AAA AAAA

o

b /

(

.........)

p

2

2

Var( P)

2

4

8

16

P

P

2

2

2

2

*

Co (

v O, P) h

1  AA AAA AAAA

o

b /

(

.........)

p

2

Var( P)

2

4

8

16

P

Assuming an absence of epistasis we have

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Agricultural Science

2

1 

2

2

A h

o

b / p

2

 1

(

Cov O, P)

2

2

2

A , giving

P

2

h  2 o

b / p

3.2 Full-sib analysis

The covariance full-sib analysis is equal to:

1 2

1 2

1 2

1 2

1 2

1 2

(

Cov FS)      

 

 

......)

2 A 4 D 4 AA 8 AD 16 DD 8 AAA

2

(

Cov FS) h

1 1

2

1 2

1 2

1 2

1 2

(   

 

 

.....)

2

2

2

 4 D 4 AA 8 AD 16 DD 8 AAA

P

P

So, when epistasis was considered negligible

1 2

Co (

v FS)  

2 A

2

2 Co (

v FS)

h

2

P

3.3 Half-sib analysis

Based on half-sib analysis, narrow-sense heritability was calculated as:

1 2

1 2

1 2

(

Cov HS)   

 ......

4 A 16 AA 64 AAA

2

(

Cov HS) h

1 1

2

1 2

(

 ......)

2

2

4

 16 AA 64