ADAPTION OF CONSERVATION AGRICULTURE IN UZBEKISTAN
Boboev Hasan, Yoshiro Higano
Doctoral Program of Sustainable Environmental Studies, Graduate School of Life and Environmental
Sciences, University of Tsukuba, Tennodai, Tsukuba, Japan
ABSTRACT

Uzbek agriculture has some serious problems related to management of water and soil quality.
The soil in many areas showed signs of severe degradation such as compaction, high degreeof salinity, and low fertility, which causes to decrease the productivity of agricultural
productions to 20-30 percent for the last decades. This is the main rationale for changing producing
agricultural productions in a sustainable way by which adapting an innovative technology such asConservation Agriculture. This paper analyzes the current adaption state/level of Conservation
Agriculture practices of farmers through addressing with socio-economic, institutional andenvironmental factors that influence on the farmers` decision making, and reveals the prior factors thatencourage or constraints the extension of CA in Uzbekistan. Logistic Regression and Probit models
are applied to analyze those factors through ranking and categorizing the relative dependent variables.
The model estimation is based on the survey conducted by mains of questionnaire, which includes
farm household (age, education, knowledge level, awareness, attitude toward conservation practices,motivation) and biophysical characteristics crop land area, crop yield, soil moisture, bulk density, soil
humus, and finally, farm financial characteristics; farm income, off farm activities, employment, andagricultural machinery). The investigation outcomes indicated that the variables such as size of farmor LAND and EMPLOYMENT RATE possess positive correlation and those were found one of themost sensitive factors for the adaption rate. While variables of SOIL_MOISTURE and SOIL HUMOUS
are in the next rank to induce farmers to adapt sustainable technology, OFF FARM ACTIVITY,
however, is prior factor that halts farmer’s adaption. It was concluded that farmers’ participation inextension-education courses, usage different level of information sources and communication channels
are also strong trigger to the implementation and extension of innovative technology in Uzbekistan.
Key words: Conservation Agriculture, sustainable agriculture, Logistic Regression and Probit model,
independent variables, environmental factors, adaption of innovative technology.
INTRODUCTION
Economy of Uzbekistan is heavily depends on agriculture, which accounted for 28 percent of GDP
and employed 40 percent of labor force. Arable land only comprises 11 percent of the territory. Cotton
and wheat are main crops in the country: wheat for domestic consumption and cotton for export.
Cotton accounts for 40 percent of the gross value of agricultural production. The current agricultural
condition of Uzbekistan requires an alternative innovative technology to conduct agriculturaloperations in a sustainable way such as Conservation Agriculture (CA) technology
Nowadays, however, productivity of agricultural productions reduced up to 20-30 percent for
the last decades. Land degradation, water shortage, soil salinity, soil erosion, and low soil fertility arebecoming urgent problems in Uzbekistan. Evidently intensive irrigation and badly maintained
drainage system are the primary cause of poor condition of the environment in many years. In former
USSR period, the agriculture sector of Uzbekistan was largely focused upon the cotton cultivation andbecame net exporter of cotton. The negligent attitudes towards science based technologies, race forgreater volumes of raw cotton and cotton monoculture, and extending new lands from Korakum andKyzylkum desert areas, by drawing huge amount of water from Amudarya and Syrdarya rivers, have
led to disastrous problems of agricultural lands in Uzbekistan, even were consequence for Aral Sea
catastrophe in Central Asia.
The current agricultural condition of Uzbekistan requires an alternative innovative technology to
conduct agricultural operations in a sustainable way such as Conservation Agriculture (CA)technology. CA is based on optimizing yields and profits, to achieve a balance of agricultural,
economic and environmental benefits. It advocates that the combined social and economic benefits
gained from combining production and protecting the environment, including reduced input and laborcosts, are greater than those from production alone. CA, based on integrated practices such as
zero/reduced tillage, crop rotations and permanent soil cover is becoming increasingly popular. Zerotillage, as a practice of CA, is now applied on more than 95 million ha worldwide, primarily in Northand South America. Approximately 47% of the zero tillage technology is practiced in South America,
39% is practiced in the United States and Canada, 9% in Australia and about 3.9% in the rest of theworld, including Europe, Africa and Asia.
Dramatic declining in crop yields due to above mentioned problems such as soil degradation andlow soil fertility required radical change in both agricultural policy and practices in Uzbekistan.
Recently, Uzbekistan researchers and scientists of agricultural institutions accompanied withinternational organizations (FAO, ICARDA, ZEF etc.) have launched different collaborative projects
under the objective of sustainable agricultural development.
Main goal of the research is analyzing current adaption state/level of Conservation Agriculture
practices of farmers through addressing with socio-economic, institutional and environmental factorsthat influence on the farmers` decision making, and to facilitate policy prescriptions and conceptual
framework based on scientific calculation of adaptability of the alternative technology to decrease
uncertainty and hesitation of risk aversion farmers. Conservation agriculture practice adaption ismultidimensional process. Numerous factors determine farmers’ willingness to use sustainable
practice. So studies need to be conducted in search of factors influencing adaption of the newtechnology in Uzbekistan condition. Multiplicity of factors combined with the potential interactions
between them contributes to adopting CA practices.
METHODSFor better understanding the factors affecting adaption of the sustainable technology, different
type of models and statistic analysis are applied for comprehensive investigation. Logistic Regression
and Probit models are employed to analyze the dependent variables or factors through ranking andcategorizing method to identify the sensitivity of those variables to develop or the constraint theadaption process.

According to the model instructions, a questionnaire, which includes the data of individual level
characteristics and farm structural factors characteristics, were collected through appraisal survey.
Proportional random sampling method was employed to select the particular number of farms locateddifferent agricultural regions. Data were analyzed using descriptive and inferential statistics such as
extent of mean, standard deviation, coefficient of variation, correlation analysis and stepwise
regression analysis.
Logistic regression model estimation
In statistics, logistic regression (sometimes called the logistic model or logit model) is used forprediction of the probability of occurrence of an event by fitting data to a logit function logistic curve.
Like many forms of regression analysis, it makes use of several predictor variables that may be either
numerical or categorical.

 P Y ln 
  0  1 X 1   2 X 2  1-P  Y   K X K

exp   0  1 X 1   2 X 2  K X K 

P Y 1  exp   0  1 X 1   2 X 2  K X K 

Probability of the event
occurringInterception (constant)Line gradient or coefficients
Predictor variable

Logistic regression is a variation of ordinary regression which is used when the dependent(response) variable is a dichotomous variable (i. e. it takes only two values, which usually represent
the occurrence or non-occurrence of some outcome event, usually coded as 0 or 1) and the
independent (input) variables are continuous, categorical, or both. As you can see from the formula p
is the probability that Y=1 and X1, X2,.. .,Xk are the independent variables (predictors). b0 , b1, b2, ....
bk are known as the regression coefficients, which have to be estimated from the data. Logistic
regression estimates the probability of a certain event occurring. Finally, the farmers were categorizedaccording to the factors affecting to the adaption level.
Description of experimental site in Tashkent region
Tashkent region is located to the north-east of Uzbekistan which is surrounded by Chatkal, thePskom and Ugom mountain chains from north-east and east. It is located 350-500 meters above thesea level in the northeast part of Uzbekistan between the western slopes of the Tyanshan mountain
ranges and the Syrdarya River. The primary water source is the Chirchik River that springs in themountains in Kirgezistan and traverses the oblast. It is tributary of the Syrdarya River. The total areaof Tashkent covers 15.300 square kilometers.
The climate of the region is sharp continental, with mild winter and long dry summer. The
average temperature in January is usually around – 1 to -2, and in June – about + 27 degrees. Plain
areas receive about 250 mm of annual precipitation, and the vegetation period covers approximately
210 days. The sunshine period totals 2889 hours during a year. The soil of Tashkent region is mainly
seryozem (grey loamy) and represents majority of plowed lands.

RESULTS AND DISCUSSIONS
Some factors commonly found in the literature to be related with the adoption of soil
conservation fall into two categories: (1) individual level or farm household characters of the farmers
including age, education, knowledge level, awareness, attitude toward conservation practices,motivation, etc. (2) farm structural or biophysical factors related to the adaption of conservation
practices including farm size, income, farm profitability, tenure, ect. In order to measure the adoption
level of sustainable soil conservation practices such as crop land area, crop yield, soil moisture, bulk
density, soil humus, and finally, farm financial characteristics; farm income, off farm activities,
employment, and agricultural machinery, were applied in the questionnaire for input data of model
estimation.
Table 1. Logistic regression model output to estimate factors’ priority
Variables
Crop Land
Employment
Education
Bulk density
Farmer's age
Crop yield
Soil moisture
Soil Humous
Off farm activities
Constant

B
0.154
0.128
0.113
0.58
0.55
0.004
-0.002-0.312-0.426-24.375

S.E.
0.189
0.528
0.844
0.39
0.063
0.003
0.003
5.091
1.076
10.14

Wald
0.661
0.059
0.018
2.209
0.775
2.087
0.712
0.004
0.157
5.778

Sig.
0.416
0.808
0.894
0.137
0.379
0.149
0.399
0.951
0.692
0.016

Exp(B)
1.166
1.137
1.119
1.06
1.057
1.004
0.998
0.732
0.653
0

Rank
1
2
3
4
5
6
7
8
9
0

The estimated logit model correctly predicted and classified 82% of farmers’ reactions (Figure1). Table shows that satisfying farmer demands LAND or size of farm accompanied with related
EMPLOYMENT RATE is one of the most important variables for adoption of CA. If farm’s farm size
is taken into consideration, adoption level would be increased. As a result of participation, suspicious
and hesitations on CA adaption are overcome, consequently reliability will be increased. According tofarmers’ consideration to apply alternative/new technology and arming whole farming infrastructurewith new agricultural and agronomic management is not worthy enough for small scale farms. Smallsize of farmers has less financial ability and can’t afford to invest great amount of budget for
innovation costs itself, without any additional support.
Another important factor is the accessibility to fundamental soil parameters such as
SOIL_MOISTURE and SOIL HUMOUS. In Uzbekistan condition water scarcity and soil degradation
are the key problems for agriculture sector. Due to these problems the agricultural production reduced
up to 20-30% and this indicator is even increasing continuously. So this problem is the next
motivation for farmers to shift to alternative way of cultivating which increase in water use efficiency
and soil fertility while preserving the external inputs and other resources.
The model outputs also revealed that the OFF FARM ACTIVITY is the next factor that constraint
the farmer’s adaption. Farmers who have additional business besides farming activity have no much

interest to adopt alternative technology. Because the income coming from the farming is not
significant enough and most farmers spend their spare time for gardening and farming. In theirconsideration, application of new agronomic practices requires extra afford and time consuming
process to deal with. So this group of farmers is staying still with traditional way of agriculture. The
farmer who can’t obtain expected amount of yield has no choice to change the way of agriculturaloperation .So these are prior factors farmers to adapt alternative sustainable technology to find asolution for the current agricultural problems.

Figure 1. Success rate of Conservation Agricultural adaption
According to the final result of model estimation, the probability of overall success rate is equalto 82%. Out of 20 observed farmers, who adopted CA practices, 15(75%) are succeeded with (P<0.5)higher probability indicators, however 5 farmers showed negative results (figure2). The prediction for
30 not-adapted farmers indicated that 26(86.7%) farmer possesses higher probability to adopt newtechnology. Only 4 farmers probability rate to adoption was lower than restriction line (P<0.5).
The table depicts the list of the farmers with low rate in success with CA and not adopted
farmers with low probability to predict the adaption of CA technology. It can be noticed that thesegroup of farmers mostly who have additional business and have small area of agricultural crop land.
CONCLUSION
In this study, factors that affect the adoption of CA by farmers are evaluated using logistic regression
model and results are presented. These results are considered to prove useful in creating projects that
optimize farmer satisfaction. According to these results, the most important factors discovered in CA
adoption are: LAND_SIZE,SOIL MOISTURE, SOIL, HUMUS and OFF FARM ACTIVITIES .

Innovation adoption is faster and more frequent among farmers with a higher level of education,interested in land acquisition to increase their farm size and on farms with a better structure. Out of all
personal characteristically factors which are hypothesized the land size is likely to have an impact

upon the adoption of best practice in agriculture and showed positive correlation to the adoption.
Social participation such as membership in local organization has a positive relation with the use ofconservation practices if the government supports the development of CA technology. Communicationand linkage between landholders and other activities may increase the adaption rate as well. Hence,
participation in extension-education courses, using level of information sources and communication
channels are considered preliminary step for better implementation of CA. It would be helpful if thegovernment encourages the use of CA practices to enhance agricultural production of salt- anddrought-affected regions, and continues the promotion of CA in dryland areas. Training and advocacy
materials should be developed for promoting CA practices in different type of crops, especially cotton
and wheat.
BIBLOGRAPHY
Dumanski, J., 2006. The paradigm of conservation tillage; Soil and Water Conservation, P1: 58-64.
FAO. 2006. Project FAO/TCP/UZB/3001 Enhanced productivity of cotton-wheat systems through the
adoption of conservation agriculture practices in Uzbekistan.
FAO. 2007. Aral Sea Manual. Conservation Agricultural practices in the drought-affected
region of Karakalpakstan.
Ahmad Rezvanfar, Atry Samiee and Elham Faham 2009. “Analysis of factors affecting adoption ofsustainable soil conservation practices among wheat growers”, J. World Appl. Sci.6 (5): 644651; Faculty of Agricultural Economics and Development, University of Tehran, Karaj, Iran.
Aziz Nurbekov, (ICARDA) 2008. “Manual on conservation agriculture practices in Uzbekistan”.
Tashkent, Uzbekistan.
Calatrava Leyva J., J. A. Franco Martínez2 and M. C, 2007. “Analysis of the adoption of soil
conservation practices in olive groves: the case of mountainous areas in southern Spain” ,Institution Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Spanish
Journal of Agricultural Research 2007 5(3), 249-258.
David J. Pannel, 2007. “On the use of individual-farm models for analysis of resource conservation
problems”, Agriculture and Resource Economics, the Unicersity of W. A., Nedlands.
Derpsch, R. 2005. The extent of Conservation Agriculture adoption worldwide: Implications andimpact. Proceedings of the Third International Congress of Conservation Agriculture. Nairobi,
Kenya.
Dumanski, J., 2006. The paradigm of conservation tillage. Proc. World Assoc. Soil and Water
Conservation, P1: 58-64.
Duncan Knowler and Ben Bradshaw 2006. ”Farmers’ adoption of conservation agriculture: A review
and synthesis of recent research”, School of Resource and Environmental Management, Semon
Fraser, University Burnaby, BC, Canada.