Relationships among Rotational Intensity, Crop Diversity Index, Land Utilization Index and Yield Efficiency in Bio-intensive and Conventional Farming Systems

Nepalese Journal of Agricultural Sciences
2011, volume 9, ISSN 2091-042X

44
Relationships among Rotational Intensity, Crop Diversity
Index, Land Utilization Index and Yield Efficiency in
Bio-intensive and Conventional Farming Systems

B.P. Rajbhandari
HICAST, Kathmandu, Nepal
bprajbhandari@gmail.com

ABSTRACT

This study was conducted in Udayapur district in 2008. A questionnaire-based survey
with 100 household heads along with field visit and personal interaction with the
concerned farmers were conducted to gather required information. Data were analysed
to compute Rotational intensity (RI), crop diversity index (CDI), land utilisation index
(LUI), and yield efficiency (YE). Regression analyses were done to reveal relationships
among these traits. BIFS farmers were found to practice scientific crop rotation
substantially more than CFS farmers both in rice-based and maize-based cropping
systems. In this study, higher RI, CDI, LUI and YE were found in bio-intensive farming
system (BIFS) as compaared to conventional farming system (CFS). The study has
revealed strong positive relationship of RI with CDI and LUI in BIFS, CFS and in
general. Cropping system both in BIFS and CFS with scientific crop rotation that ensures
higher CDI and LUI is recommended for increasing yield efficiency. Direct positive
relationship among RI, CDI and LUI has been revealed by this study as a rule and has
suggested to be used in validating yield efficiency of optonal farming system as compared
to the mainstream conventional farming system.

Key words: Scientific crop rotation, Rotational Intensity (RI), Crop Diversity Index
(CDI), Land Utilization Index (LUI), Yield Efficiency (YE), Regression analysis

INTRODUCTION

Proponents of Green Revolution or High Chemical Inputs Agriculture System (HCIAS)
generally argue that developing countries should opt for an agro-industrial model that
relies on standardized technologies and ever-increasing use of the chemical fertilizers and
pesticides to provide additional food supplies for growing population. In contrast, a
growing number of farmers, agro ecologists, NGOs and analysts propose that instead of
the capital and petro-chemical input-intensive as well as environment-degrading
approach, developing countries should favour an agro-ecological and socio-economic
model (Altieri, M. A., P. Rosset, & L. A. Thrupp, 1998, Rajbhandari, 2000). Many of the
techniques that comprise the biointensive method were present in the agriculture of the
ancient Chinese, Greeks, Mayans, and of the Early Modern period in Europe. Alan

Nepalese Journal of Agricultural Sciences
2011, volume 9, ISSN 2091-042X

45
Chadwick brought together the Biodynamic and French intensive methods, as well as his
own unique approach, to form what he called the Biodynamic-French Intensive method.
The method was further developed by John Jeavons and Ecology Action into a
sustainable 8-step food-raising method known as "GROW BIOINTENSIVE". Today bio-
intensive farming is a system that emphasizes biodiversity, recycling of nutrients,
synergy among crops, animals, soils, and other biological components, and regeneration
and conservation of resources. In other words, the concept and approaches of BIF system
is based on holistic system of sustainable management of natural resources in a given
agro-ecosystem with specific cultural and knowledge base (Rajbhandari & Gautam,
1998). The principles of BIF system include scientific crop rotation; mixed farming
systems; optimization of organic recycling; participatory and sustainable management of
natural resources including biodiversity; participatory research and extension; and
attainment of high degree of self-reliance of farm households against external techno-
economic shocks (Rajbhandari, 2000). The bio-intensive approach is initially more
labour-intensive than conventional approaches, and therefore best suites to small scale
family centred food production in urban or rural settings (ILEIA, 1989). The bio-
intensive farming system is a biologically intensive mixed farming system, which relies
on intensive engagement of farmers, organic recycling optimization through intensive
scientific crop rotations, integrated plant nutrient management (IPNM), integrated pest
management (IPM). The bio-intensive farming system relies on appropriate spatial
management of field crops, vegetable crops, fruits and fodder trees as well as livestock
and poultry for rational and ecologically non-destructive utilization of lands.
Furthermore, it increases the soil fertility, revitalizes the degraded soil, decreases
environmental pollution and prevents health hazards to humans and livestock as well as
reduces further degradation of the environment, which otherwise might lead to
desertification of the globe. It is, therefore, not only eco-friendly but also friendly to
human and animal health (Rajbhandari, 2010 b).

The technique behind adopting bio-intensive farming is that cropping systems and
techniques specially tailored to the needs of specific agro-ecosystems are based on local
inputs and techniques with each combination fitting to particular ecological resources by
combining different components of the farming system (plants, animal, soil, water,
climate and people) in order to optimize the synergistic interaction among the
components (Rajbhandari, 2010 b). In this approach, performance criteria include not
only increased production but also properties of sustainable food security, biological
stability, resource conservation and equity (UNDP, 1995). However, there is not a single
and simple method of validatng efficiency of the alternate farming system like BIFS as
compared to the mainstream conventional (petro-chemical based) farming system.

This study was conducted to estimate and analyze the relationships among rotational
intensity, land utilization index, crop diversity index and yield efficiency, and find out the
means of validating efficiency of optional farming system (bio-intensive farming) as
compared to the mainstream conventional farming system.

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2011, volume 9, ISSN 2091-042X

46
MATERIALS AND METHODS

WOREC Nepal has been implementing bio-intensive farming system in Udayapur for the
last 15 years; and therefore this district was selected as the study site. This study was
conducted in 2008 covering four VDCs i.e. Rauta, Triveni, Hadiya and Jogidaha and one
Triyuga municipality. Sample households were taken based on the farmer's engagement
in bio-intensive farming system (BIFS) and conventional farming system (CFS). The
total sample size was 100 households, i.e. 50 households from each system (10
households per VDC/municipality). Purposive random sampling technique was used to
select the required number of households from both systems.


Figure 1 Map of Udayapur showing study areas (shaded areas)

The study was based on the primary data collected with the help of semi-structured
questionnaire and published secondary information. The questionnaire was developed to
gather relevant information required to meet the specific objectives. The questionnaire
was first pre-tested with farmers for its accuracy and clarity. Then it was finalized by
incorporating farmers’ suggestions. The collected data were grouped, coded and entered
into the computer for processing. Computer software MS excel was used to analyse the
data. Mean value, standard deviation (SD) and R2 were obtained to interpret the results.
Similarly informations were also used to calculate Rotational Intensity (RI), Land
utilization index (LUI), Crop diversity index (CDI), and Yield efficiency (YE). These
were computed using the formulae given below. The number of crops grown in 5-year
rotation, and respective areas and crop yields were taken into consideration. Regression
plains were drawn and R2 determined to estimate relationship between various
parameters.

RI = No. of crops grown in a rotation X 100
Duration of rotation

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2011, volume 9, ISSN 2091-042X

47
n
LUI =∑ai di /A×365
i=1
Where, ai=Area occupied by ith crop
di =days occupied by ith crop
A=Total cultivated area available for 365 days (1 year)
n=Total number of crops gown per year
CDI =1-∑x2 / (∑x) 2
Where, x=Area under the individual crop
YE = Ya / Yl ×100
Where, Ya= Yield per unit area of the farm
Yl=Yield per unit area of locality

RESULTS AND DISCUSSION

Scientific crop rotation

Scientific crop rotation is one of the important agro-techniques employed in order to
reduce the pest incidence in the field as well as sustain crop yield. It was found that 68
percent of the BIFS adopting households followed the scientific crop rotation but in the
case of CFS adopting households the case was just opposite. Sixty four percent of the
households employing CFS had not followed scientific crop rotation (Figure 2). Those
who did not followed the crop rotation usually used to grow the same vegetable (in the
rice-based and maize-based cropping systems) in the same season, which actually
provided ground for pest incidence.


Figure 2 Practice of scientific crop rotation by the respondents (%)

Rotational intensity (RI), CDI, LUI and YE

Computed mean values and standard deviations of rotational intensity (RI), crop diversity
index (CDI), land utilization index (LUI) and yield efficiency (YE) in bio-intensive
farming system and conventional farming system are presented in Table 2.







0 20 40 60 80
1
2


Fa
rm
er
's

ca
te
go
ry
:
1
B
IF
S;

2
CF
S
Percentage

Followed Not followed

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2011, volume 9, ISSN 2091-042X

48
Table 2 Mean values and SD of RI, CDI, LUI and YE in BIFS and CFS
Farming system

RI, % CDI LUI

YE, %
Mean 440 0.70 0.91 123
BIFS SD 54.77 0.054 0.035 12.29
Mean 260 0.44 0.65 84
CFS SD 54.77 0.051 0.055 11.00

It is evident from the table 2 that RI was only 260 percent in CFS, where farmers used to
grow only 2-3 crops in the annual pattern, while in case of BIFS it was 440 percent. The
BIFS adopting farmers used to grow 4 or more crops in the annual cropping pattern.
Owing to higher RI the BIFS had higher CDI (0.70) than in CFS (0.44). Likewise, the
BIFS had higher LUI (0.91) and YE (123%) as compared to the CFS (LUI = 0.65, YE =
84%). Rajbhandari (2010 a) and Duwal (2008) have shown that CDI and LUI have direct
positive relaionship with yield efficiency. BIFS farmers had higher return due to the
higher RI, CDI and LUI. This clearly shows the supremacy of bio-intensive farming
system based on integrated sustainable approach over conventional farming system in
Udayapur.

Relationship among RI, CDI and LUI

Regression plains computed among various pairs of quantitative traits have revealed
strong positive relationship of RI with CDI and LUI and that of CDI with LUI in general
(in both systems together) and in both systems- BIFS and CFS- separately (Figure 3 to
Figure 11). The computed value of R2 in all pairs of traits in both systems combined
(general) and separately was higher than 0.900.

Relationship RI and LUI in
both farming systems
y = 0.0013x + 0.336
R2 = 0.9531
0
0.5
1
1.5
2
0 200 400 600
RI
LU
I

Figure3 Figure 4
Figure 3 Relationship between RI & CDI in both farming systems combined
Figure 4 Relationship between RI & LUI in both farming systems combined

Relationship of RI and CDI in
both farming systems
y = 0.0013x + 0.1167
R2 = 0.9732
0
0.2
0.4
0.6
0.8
1
1.2
0 200 400 600
RI
CD
I

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2011, volume 9, ISSN 2091-042X

49
Rlationship of CDI and LUI
in both farming systems
y = 0.9856x + 0.2192
R2 = 0.9874
0
0.5
1
1.5
0 0.5 1
CDI
LU
I

Relationship betw een RI and
CDI in CFS
y = 0.0009x + 0.21
R2 = 0.924
0
0.2
0.4
0.6
0.8
0 200 400
RI
CD
I

Figure 5 Figure 6
Relationship betw een RI and
LUI in CFS
y = 0.001x + 0.3983
R2 = 0.9734
0
0.2
0.4
0.6
0.8
0 200 400
RI
LU
I

Relationship betw een CDI and
LUI in CFS
y = 1.038x + 0.1931
R2 = 0.9509
0
0.5
1
1.5
0 0.2 0.4 0.6
CDI
LU
I

Figure 7 Figure 8
Relationship of RI and CDI in
BIFS
y = 0.001x + 0.2633
R2 = 0.99
0
0.2
0.4
0.6
0.8
1
0 200 400 600
RI
CD
I

Relationship of RI and LUI in
BIFS
y = 0.0006x + 0.6367
R2 = 0.9351
0.8
0.85
0.9
0.95
1
0 200 400 600
RI
LU
I

Figure 9 Figure 10

Figure 5 Relationship between LUI & CDI in both farming systems combined
Figure 6 Relationship between RI and CDI in CFS

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2011, volume 9, ISSN 2091-042X

50
Relationship of CDI and LUI
in BIFS
y = 0.6365x + 0.465
R2 = 0.973
0.8
0.85
0.9
0.95
1
0.6 0.65 0.7 0.75 0.8
CDI
LU
I

Figure 11

Figure 7 Relationship between RI and LUI in CFS
Figure 8 Relationship between LUI and CDI in CFS
Figure 9 Relationship between RI and CDI in BIFS
Figure 10 Relationship between RI and LUI in BIFS
Figure 11 Relationship between LUI and CDI in BIFS

Similar trends in the relationship among CDI, LUI and YE were reported by Rajbhandari
(2010a). Postive relationship between cropping intensity and CDI was reported by
Shahidullah et al (2006). The findings of this study were at par to those described by
Rajbhandari (2010 a), Duwal (2008) and Shahidullah et al (2006).

Regression analysis has also revealed strong negative relationship of crop rotation and
LUI with severity (incidence) of pest damage as reported by Duwal (2008) and
Rajbhandari (2010 a). Obviously, in the CFS where rotational intensity and LUI is lower,
the pest damage to yield is substantial. This is an important reason that yield has been
declining in the farms where the use of only chemical fertilisers along with pesticides are
continued without following scientific crop rotation. The farmers adopting BIFS have
reported lesser incidence of crop damage by pests, and they have higher cropping
intensity, CDI and YE (Duwal, 2008).

The RI, CDI and LUI had direct positive relationship in general (Figure 12) as well as in
both farming systems separately (Figure 13 & 14). Thus direct positive relationship
among RI, CDI and LUI has been revealed by this study as a rule that might be used in
validating yield efficiency of optonal agricultural system, eg BIFS as compared to the
mainstream conventional agricultural system.

Nepalese Journal of Agricultural Sciences
2011, volume 9, ISSN 2091-042X

51
Relationship among RI, CDI and
LUI in both farming systems
0
100
200
300
400
500
RI
CDILUI

Figure 12


Relationship among RI, CDI and LUI
in BIFS
0
100
200
300
400
500
RI
CDILUI

Figure 13 Figure 14

Figure 12 Relationships among RI, CDI & LUI in general (both farming
systems combined)
Figure 13 Relationships among RI, CDI and LUI in CFS
Figure 14 Relationships among RI, CDI and LUI in BIFS

CONCLUSION

It is thus obvious from the findings that scientific crop rotation increases crop diversity
and land utilization, and consequently the total crop yield in a given locality both under
bio-intensive and conventional farming systems. Bio-intensive farming may be one of the
best options to govern this relationship positively in favour of ecology and environment
protection as well as food production and human health.

Relationship among RI, CDI and LUI in
CFS
0
100
200
300
RI
LUICDI

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2011, volume 9, ISSN 2091-042X

52
Direct positive relationship among RI, CDI and LUI has been revealed by this study as a
rule that might be used in validating yield efficiency of optonal agricultural system as
compared to the mainstream conventional agricultural system.


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