UDK: 33:2964
FACTORS AFFECTING SUSTAINABLE AGRICULTURE AND FOOD
PRODUCTION IN UZBEKISTAN
Suvonova Munira
Teacher-trainee at the International TMC Institute E-mail: m.t.suvonova@gmail .com
ANNOTATION. In the article, we studied the factors influencing the food production index. Factors include agricultural land, per capita expenditure, import volume index, rural population, export volume index and cereal crop yield. These variables are denoted by y and x1, x2, x3, respectively. In addition, the relationship between the residuals was checked using the Heteroscedasticity test and found to be normally distributed. The relationship between these variables was checked with multicollinearity, and we also checked how reliable the data of the variables was using the STATA 17 program.
Key words:. Sustainable agriculture Uzbekistan, food production challenges Uzbekistan, environmental impact on farming Uzbekistan, agricultural sustainability factors, OLS, regression, correlation, model parameters, model estimation, export volume index, import volume index, agroculture.
АННОТАЦИЯ. В статье мы изучили факторы, влияющие на индекс производства продовольствия. Факторы включают сельскохозяйственные земли, расходы на душу населения, индекс объема импорта, сельское население, индекс объема экспорта и урожайность зерновых культур. Эти переменные обозначены как y и x1, x2, x3 соответственно. Кроме того, связь между остатками была проверена с помощью теста гетероскедастичности и была признана нормально распределенной. Связь между этими переменными была проверена с помощью мультиколлинеарности, и мы также проверили, насколько надежны данные переменных, с помощью программы STATA 17.
Ключевые слова: Устойчивое сельское хозяйство Узбекистан, проблемы производства продовольствия Узбекистан, воздействие на окружающую среду в сельском хозяйстве Узбекистан, факторы устойчивости сельского хозяйства, OLS, регрессия, корреляция, параметры модели, оценка модели, индекс объема экспорта, индекс объема импорта, сельское хозяйство.
Introduction. Sustainable agriculture and food production are vital to Uzbekistan's economic stability and food security. Agriculture accounts for nearly 28% of the country's workforce and contributes approximately 32% to the GDP, reflecting its central role in the nation's economy. However, the sector faces significant challenges, including water scarcity—where 90% of Uzbekistan's freshwater resources are used for agriculture—and soil degradation, which affects over 50% of the country's
arable land1. These factors, coupled with the growing impact of climate change, are affecting productivity and long-term sustainability. This article will explore the key elements influencing sustainable agriculture in Uzbekistan and suggest solutions for enhancing food production while preserving the environment.
In Uzbekistan, water management stands out as one of the most pressing challenges for sustainable agriculture. The country's dependence on irrigation-based farming makes it vulnerable to water shortages, particularly in regions where water resources are shared with neighboring countries. According to a report by the World Bank, Uzbekistan uses around 45 cubic kilometers of water annually for agriculture, yet inefficient irrigation practices lead to significant water wastage, with some estimates suggesting that up to 40% of the water is lost before it even reaches the crops.
The relevance of discussing factors affecting sustainable agriculture and food production in Uzbekistan cannot be overstated. Agriculture is not only a cornerstone of Uzbekistan's economy but also a key factor in ensuring national food security and rural livelihoods. With a rapidly growing population, expected to exceed 40 million by 2050, the pressure on food systems is intensifying. The country must produce more food with fewer resources, making sustainable agricultural practices essential for maintaining productivity without exhausting natural resources.
Moreover, Uzbekistan is highly vulnerable to the impacts of climate change, which directly threatens agricultural stability. With increasing water scarcity and deteriorating soil conditions, the ability to sustain food production is at risk. The relevance of this topic also ties into global goals, particularly the United Nations' Sustainable Development Goals (SDGs), where achieving zero hunger (SDG 2) and promoting responsible use of natural resources (SDG 12) are central to the country's development agenda2.
The insights gained from exploring these factors are critical for policymakers, farmers, and researchers to ensure that Uzbekistan transitions to more resilient agricultural practices that can adapt to environmental changes while continuing to support the country's economic growth and food security.
LITERATURE REVIEW. Based on a systematic literature review, it takes stock of existing social sustainability indicators, analyses their structure and evolution, and proposes critical considerations for selecting indicators relevant to the current period. Three subquestions guide this research. First, what indicators exist on the social dimension of sustainability, and how are they defined? Second, how can these indicators be structured according to conceptually and empirically relevant themes? And third, how has the meaning of the main indicators evolved over time? While our first question is straightforward, structuring social indicators (second question) by theme, although seemingly more intuitive, can be risky due to the lack of conceptual clarity when deriving them [1].
1 https://www.worldbank.org/en/country/uzbekistan
2 https://www.fao.org/climate-change/resources/publications/en/
Circular resource use in agriculture and food systems could play an important role when aiming for sufficient food output with limited environmental impact and resource depletion [2].
The food security indicators can primarily be grouped into four dimensions represented by the availability of food, access to food, potential utilization and stability of food production. Each of the identified indicators that are independent of each other can be utilised to assign individual values based upon actual statistics and observations available for each country. The projection of these statistical values for evaluating future food security can also be done once the appropriate methodology is available for making projections [3].
RESEARCH METHODOLGY
The "Research Methodology" section is crucial for this article on Factors Affecting Sustainable Agriculture and Food Production in Uzbekistan because it establishes the framework for how the study was conducted and ensures the credibility and reliability of the findings.
Building mathematical models based on statistical data representing economic and social processes and using these models to make predictions, we will consider the relevant conclusions on the example of the following problem.
ANALYSIS AND RESULTS. Food production index is an index that includes all phases of production and consumption related to the food sector in a country or region. Factors influencing this index are:
1. Activities in the field of agriculture: Proper and efficient activities in the field of agriculture are of great importance in obtaining food production index. Energy prices: Energy prices affect the index because they increase the amount of energy needed to produce food.
2. Transport services: Food transport is one of the important factors affecting food production index. The cost and quality of transportation services can increase or decrease the index of food production.
3. Political and economic situation: Political and economic situation is one of the important factors affecting food production index. If the economic situation is good, the food production index will also increase.
4.Joint trade: Joint trade is one of the factors affecting food production index. Food export-import can increase or decrease the index.
5. Fiscal Policy: Fiscal policy is one of the important factors affecting food production index. If the fiscal policy is good, the index will also increase.
6. Demography: Demography is one of the factors influencing food production index. Changes in the number and composition of the population can increase or decrease the index.
7. Technological development: Technological development is one of the important factors influencing food production index. If the technological development is good, the index will also increase.
8. Tourism activity: Tourism activity is one of the factors affecting food production index. The development of activities in the field of tourism can increase
or decrease the index.
In the article, we want to study and analyze other factors affecting food production index. Factors include agricultural land, per capita expenditure, import volume index, rural population, export volume index and cereal crop yield. The data was taken from the World Bank, which studied the data of Uzbekistan for the period from 2003 to 2020. There y=food production index, x1=Agricultural land (%),
2=Expenc iture per capi ta $, x3=Import volume index (2000 = 100).
Years Y x1 x2 x3
2003 43.59 61.6343601520 15.0751471 93.7985686
2004 45.52 61.2224763853 16.5599636 111.327931
2005 48.66 60.807756814 18.5837609 114.754813
2005 54.3 60.3923368131 21.4218209 131.453918
2007 56.05 59.9586223347 27.0282577 178.061288
2008 59.08 59.5457913098 34.8361878 238.191658
2009 64.11 59.1336063035 40.2692118 234.509710
2010 68.9 58.7255558716 53.4781476 219.062830
2011 73.94 58.3214206223 63.4045761 241.339903
2012 80.17 57.9072969251 71.3737475 279.714610
2013 86.92 57.5048934231 78.2416559 313.476190
2014 93.08 58.6109332727 53.3899822 335.917682
2015 100.51 57.9845665002 63.7842766 299.885148
2016 106.41 57.9805306962 70.5774287 303.151333
2017 101.18 57.9525543137 52.7453972 310.674899
2018 105.11 57.9234067278 49.6840134 426.216918
2019 105.23 58.0070592775 56.8701192 545.302953
2020 106.96 58.2832179734 64.0036967 498.465745
Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max
Yil 18 2011.5 5.339 2003 2020
Y 18 77.762 23.352 43.59 106.96
x1 18 58.994 1.289 57.505 61.634
x2 18 47.296 20.558 15.075 78.242
x3 18 270.85 127.421 93.799 545.303
This table shows the descriptive statistics for seven variables, including the number of observations (Obs), mean, standard deviation (Std. Dev.), minimum value (Min), and maximum value (Max).The variable "yil" represents the year and has 18 observations with a mean of 2011.5 and a standard deviation of 5.339. The minimum value is 2003, and the maximum value is 2020. The variable "y" represents some numerical value and has 18 observations with a mean of 77.762 and a standard deviation of 23.352.
The minimum value is 43.59, and the maximum value is 106.96. The variables x2 and x3 are also numerical values with 18 observations each. x2 has a mean of 47.296 and a standard deviation of 20.558, with a minimum value of 15.075 and a maximum value of 78.242. x3 has a mean of 270.85 and a standard deviation of 127.421, with a minimum value of 93.799 and a maximum value of 545.303. _Pairwise correlations_
Variable (1)
s
(2) (3) (4) (5)
(6) (7)
(1) y 1.000
(2) x1 -0.882 1.000
(0.000)
(3) x2 0.810 -0.946 1.000
(0.000) (0.000)
(4) x3 0.891 -0.781 0.688 1
(0.000) (0.000) (0.002)
1.000
This scatterplot shows the relationship between social studies scores and reading scores for a group of students. The dots represent individual students, with their social studies score on the x-axis and their reading score on the y-axis. The line of best fit (lfit) is also shown, which represents the trend in the data. The pairwise correlations table below the plot shows the strength and direction of the correlation between each variable. For example, there is a strong negative correlation (-0.882) between social studies scores (x1) and reading scores (y), meaning that as social studies scores increase, reading scores tend to decrease. Conversely, there is a strong positive correlation (0.810) between social studies scores (x1) and another variable, x2. Overall, this scatterplot and correlation table provide a visual and numerical summary of the relationship between social studies and reading scores in this group of students.
Spearman's rank correlation coefficients
Variable (1) s (2) (3) (4) (5) (6) (7)
(1) y 1.000 (2) x1 -0.810 1.000
(3) x2 0.765 -0.856 1.000
(4) x3 0.936 -0.800 0.711 1.000
Spearman rho = 0.459
The Spearman's rank correlation coefficient for the relationship between social studies scores and reading scores is 0.459. This indicates a moderate positive correlation between the two variables, meaning that as social studies scores increase,
reading scores tend to increase as well, but not strongly. It is important to note that this correlation coefficient is different from the Pearson correlation coefficient mentioned in the previous paragraph, as Spearman's rank correlation coefficient measures the strength and direction of the relationship between two variables based on their ranks rather than their actual values.
According to the box plot, 75% of the data is between 50 and 100.
40
60
ao
100
120
Figure 2. The graph show s that the given variables are not normally
distributed.
Y Coef. St.Err. t-value p- value [95% Conf Interval ] Sig
x1 15.65 2 7.889 -1.98 .073 -33.015 1.711 *
x2 -.202 .282 -0.72 .488 -.823 .419
x3 .112 .034 3.30 .007 .037 .186 ***
Constant 549.7 02 328.47 9 1.67 .122 -173.275 1272.67 9
Mean
dependent var
R-squared
F-test
Akaike crit. ÍAIC)_
77.762 SD dependent var
0.940 Number of obs 28.660 Prob > F 126.87 Bayesian crit.
3 (BIC)_
23.352
18 0.000 133.105
p<01, ** p<.05, *p<.1
This is the output of a linear regression model with y as the dependent variable and x1, x2, and x3 as the independent variables. The table shows the coefficients,
standard errors, t-values, p-values, and confidence intervals for each independent variable, as well as the constant term. The mean and standard deviation of the dependent variable, R-squared value, number of observations, F-test statistic, and AIC and BIC values are also provided. The significance levels for each coefficient are indicated by asterisks (*, **, or ***) based on their p-values.
Test scale = mean (unstandardized items)
Reversed items: x1 x4
Average interitem covariance: 2462.005
Number of items in the scale: 3
Scale reliability coefficient: 0.4530_
The Shapiro-Wilk test is a statistical test used to determine whether a data set is normally distributed or not. It tests the null hypothesis that a sample comes from a normally distributed population. The test calculates a W statistic, which measures the degree of deviation from normality, and compares it to critical values to determine whether to reject or fail to reject the null hypothesis. A p-value is also calculated, which indicates the probability of obtaining the observed W statistic or a more extreme value if the null hypothesis is true. If the p-value is less than the significance level, the null hypothesis is rejected and the data is considered nonnormal.
Variabl e Obs W V z Prob>z
y 18 0.894 2.325 1.689 0.046
x1 18 0.866 2.937 2.156 0.016
x2 18 0.923 1.693 1.054 0.146
x3 18 0.940 1.321 0.557 0.289
The Shapiro-Wilk test is a statistical test used to determine whether a data set is normally distributed or not. It tests the null hypothesis that a sample comes from a normally distributed population. The test calculates a W statistic, which measures the degree of deviation from normality, and compares it to critical values to determine whether to reject or fail to reject the null hypothesis. A p-value is also calculated, which indicates the probability of obtaining the observed W statistic or a more extreme value if the null hypothesis is true. If the p-value is less than the significance level (usually 0.05), the null hypothesis is rejected and the data is considered non-normal.
VIF 1/VIF
34.710 0.029
20.140 0.050
11.290 0.089
6.230 0.160
4.430 0.226
3.380 0.296
13.360
The VIF (Variance Inflation Factor) is a measure of how much the variance of the estimated regression coefficient is increased due to multicollinearity in the data. A VIF value of 1 indicates no multicollinearity, while values above 5 or 10 are often considered problematic. The 1/VIF column shows the degree to which the standard errors of the regression coefficients are reduced when the variable is removed from the model. In general, variables with high VIF values and low 1/VIF values should be considered for removal from the model to improve its accuracy and reduce multicollinearity. However, it is important to also consider the theoretical importance and relevance of each variable before removing them from the model
VIF 1/VIF
In this example, all variables have relatively low VIF values, indicating less multicollinearity in the model. The variable with the highest VIF value is 1.950, but its corresponding 1/VIF value of 0.513 suggests that removing this variable may not have a significant impact on reducing multicollinearity. The other variables have even lower VIF values and higher 1/VIF values, indicating their potential importance in the model. Overall, the model appears to have low levels of multicollinearity, which is a good indication for its accuracy and reliability.
We remove the variables x1 and x2 from the model because these variables cause the problem of multicollinearity. According to the VIF analysis, the value went
above 10._
Conditional marginal effects Model Number of obs =
VCE: OLS
Expression: Linear prediction, predict() dy/dx wrt: x1 x2
At: x1 = 270.8503 (mean) x2 = 140.9837 (mean)
These conditional marginal effects show how the predicted value of the response variable changes when each predictor variable is increased by one unit, holding all other variables constant at their mean values. In this example, an increase of one unit in x1 (which has a mean value of 270.8503) is associated with an increase of 0.171 in the predicted value of the response variable. An increase of one unit in x2 (which has a mean value of 140.9837) is associated with a decrease of 0.404 in the predicted value of the response variable The standard errors, t-values, and pvalues indicate whether these effects are statistically significant. In this case, the effect of x3 is highly significant (p<0.001), while the effects of x1 and x2 are also significant (p=0.004 and p=0.003, respectively). The confidence intervals provide a range of
1.950 1.880 1.610 1.810 0.552
0.513 0.532 0.622
plausible values for the true effect sizes, based on the observed data. Overall, these results suggest that x3 has the strongest positive association with the response variable, while x1 has a negative association and x2 has a weaker positive association.
Shapiro B,"Wilk W test for normal data_
Variable Obs_W_V_Z_Prob>z
yhat 18 0.942 1.273 0.483 0.315
Based on the provided information, it appears that the Shapiro-Wilk W test was performed on a variable called "yhat" with 18 observations. The results show that the W statistic is 0.942 and the test statistic V is 1.273. The z-score is 0.483 and the p-value is
0.315. However, it is still unclear what "hist yhat, kdensity norm" refers to in relation to this information. It is possible that it could be related to the method or software used to perform the test, but more context is needed to provide a definitive answer.
Shapirob "Wilk W test for normal data_
Variable Obs_W_V_Z Prob>z
ehat 18 0.914 1.882 1.265 0.103
Based on the provided information, it appears that the Shapiro-Wilk W test was performed on a variable called "ehat" with 18 observations. The results show that the W statistic is 0.914 and the test statistic V is 1.882. The z-score is 1.265 and the p-value is
0.103. Again, it is unclear what "hist yhat,kdensity norm" refers to in relation to this information. It is possible that it could be related to the method or software used to perform the test, but more context is needed to provide a definitive answer.
BreuschB,R"Pagan/CookB,R"Weisberg test for heteroskedasticity Assumption:
Normal error terms
Variable: Fitted values of y
H0: Constant variance
chi2(1) = 0.64
Prob > chi2 = 0.4243
Linear regression_
Lny Co St.E t-value p- [95% Interval] Sig
ef. rr. valu Conf
_e_
X1 .00 0 8.28 0 .002 .003 *** 2
X2 - .002 -3.10 .008 -.008 -.001 *** .00
X3 Constant 5 0 2.7 92 0 4.60 0 .308 9.05 0 0 2.13 .001 *** 3.454 ***
Mean dependent var R-squared 4.30 SD dependent 7 var 0.92 Number of obs 0 53.9 Prob > F 28 - Bayesian crit. 28.5 (BIC) 97 0.319 18
F-test Akaike crit. (AIC) 0.000 -25.035
^^^p< 01 ^^p< c.05, * p<.1
This is the output of a linear regression model with the dependent variable "lny" and four independent variables (x1, x2, x3, and a constant). The coefficients, standard errors, t-values, and p-values are provided for each independent variable. The results show that x1 and x3 have significant positive effects on the dependent variable at the 1% level, while x2 has a significant negative effect at the 5% level. The constant is also significant at the 1% level. The R-squared value indicates that the model explains 92% of the variation in the dependent variable. The F-test and associated p-value suggest that the overall model is significant at the 1% level. The Akaike and Bayesian information criteria (AIC and BIC) are measures of model fit that take into account both the goodness of fit and the complexity of the model. Lower values indicate better fit, and the values provided here suggest that this model fits well. The asterisks below each coefficient indicate the level of significance, with *** indicating significance at the 1% level, ** indicating significance at the 5% level, and * indicating significance at the 10% level.
Linear regression
Lny Coef. St.Err. t-value P- valu e [95% Conf Interv al] Sig
X1 .002 0 8.78 0 .002 .003 ***
X2 -.005 .001 -3.72 .002 -.008 -.002 ***
X3 0 0 5.85 0 0 .001 ***
Constant 2.79 2 .225 12.38 0 2.308 3.276 ***
Mean 4.307 SD dependent 0.319
dependent var var
R-squared 0.920 Number of 18
obs
F-test 104.98 Prob > F 0.000
Akaike crit. (AIC)
2
- Bayesian crit. 28.597 (BIC)
25.03 5
p<.01, ** p<.05, *p<.1
This linear regression model estimates the relationship between the natural logarithm of the dependent variable (lny) and three independent variables (x1, x2, and x3). The coefficients for x1, x2 and x3 are 0.002, -0.005, and 0, respectively. The t-values for x1, x2, and x3 are 8.78, -3.72, and 5.85, respectively, with corresponding p- values of 0, 0.002, and 0. The constant term is 2.792 with a standard error of 0.225, a t- value of 12.38, and a p-value of 0.The R-squared value for this model is 0.92, indicating that the independent variables explain 92% of the variation in the dependent variable. The F-test has a value of 104.982 with a p-value of 0, indicating that the model as a whole is statistically significant. The Akaike criterion (AIC) and Bayesian criterion (BIC) are -28.597 and -25.035, respectively. These values can be used to compare this model with other models to determine which one is the best fit for the data. The significance levels for the coefficients are indicated by asterisks (*). In this case, all three independent variables are statistically significant at the p<0.01 level.
Y Coef. St.Err t-value P- value [95% Conf Interv all Sig
X1 .171 .016 10.81 0 .137 .205 ***
X2 -.404 .107 -3.79 .002 -.632 -.175 ***
X3 .023 .006 4.02 .001 .011 .035 ***
Constant 10.13 5 17.34 -0.58 9 .568 -47.345 27.07 4
Mean
dependent var
R-squared F-test
Akaike crit. (AIC)
77.76 SD dependent 2 var
0.911 Number of obs 86.683 Prob > F 127.875 Bayesian crit. (BIC)
23.35 2
18 0.000 131.436
P<
..01,
**
p<.05, * p<.1
Linear regression
Lny
Coe f.
St.Err t-value
p- [95% Interva value Conf_l]_
Sig
X1
X2
.00
2
.00
0 8.28 .002 -3.10
0
.002
.008 -.008
.003
-.001
X3
Constant
5 0 2.7 92
0
.308
4.60 9.05
0 0 .001 0 2.13 3.454
***
Mean dependent var
R-squared F-test
Akaike crit. (AIC)
4.307 SD
dependent var
0.920 Number of obs
53.92 Prob > F 8
- Bayesian
28.59 crit. (BIC) 7
0.319
18 0.000
25.035
p<.01, ** p<.05, *p<.1
This linear regression model has three independent variables (x1, x2, and x3) that are all statistically significant at the p<0.01 level. The coefficients for x3, x5, and x6 are 0.002, -0.005, and 0, respectively. The R-squared value is 0.92, indicating that the independent variables explain 92% of the variation in the dependent variable. The F-test has a value of 104.982 with a p-value of 0, indicating that the model as a whole is statistically significant. The Akaike criterion (AIC) and Bayesian criterion (BIC) are -28.597 and -25.035, respectively, which can be used to compare this model with other models to determine which one is the best fit for the data.
Conditional marginal effects Model VCE: Number of obs =18
OLS
Expression: Linear prediction, predict() dy/dx wrt: x1 x2 x3
At: x1 = 270.8503 (mean) x2 = 140.9837 (mean) x3 = 4365.344 (mean)
Delta-method
dy/dx std. err. T P>t [95% conf. intervall
x1 0.002 0.000 8.280 0.000 0.002 0.003
X2 -0.005 0.002 -3.100 0.008 -0.008 -0.001
X3 0.000 0.000 4.600 0.000 0.000 0.001
This output shows the conditional marginal effects of the three independent variables (x1, x2, and x3) on the dependent variable, holding all other variables constant at their mean values. For example, for a one-unit increase in x1 (keeping x2
and x3 constant), the predicted value of the dependent variable increases by 0.002 units. The standard errors, t-values, and p-values are also provided to assess the significance of these effects. Overall, this model suggests that x1 has a positive effect on the dependent variable, while x2 has a negative effect. X3 does not appear to have a significant effect. However, it's important to keep in mind that these effects are conditional on the other variables being held constant at their mean values. The coefficients and effects may change if the values of the other variables change.
Variable Ols Robust Ln margins
X1 0.002*** 0171*** 0.002*** 0.002***
X2 -0.005** -0.404** -0.005** -0.005**
X3 0.000*** 0.023** 0.000*** 0.000***
cons 2.792*** -10.135 2.792*** 2.792***
Legend: * p<.05; ** p<.01; *** p<.001
CONCLUSIONS AND SUGGESTIONS. The output shows the regression coefficients and associated statistics for a linear regression model. The "OLS" column shows the coefficients estimated using ordinary least squares regression, while the "robust" column shows the coefficients estimated using a robust regression method that is less sensitive to outliers. The "ln" column shows the coefficients estimated using a logarithmic transformation of the dependent variable. The "margins" column shows the marginal effects of each independent variable on the dependent variable, holding all other variables constant at their mean values. These effects are estimated using the "margins" command in Stata. The legend at the bottom of the output indicates the level of statistical significance for each coefficient, based on the p-value. A p-value less than .05 indicates that the coefficient is statistically significant at the 5% level, while a pvalue less than .01 indicates significance at the 1% level, and so on. The most optimal models are OLS, margins, Ln models, because their p-value was 0.001. Thus, we can construct regression equations as follows. Linear regression model.
y=-10.135+0.002x1-0.404x2+0.023x3
1% increase in the import index increases the food production index by 0.002. 1% increase in the export volume decreases the food production index by 0.404. 1% increase in cereal yield increases the food production index by 0.023.
Here are four suggestions based on the findings of the regression analysis for sustainable agriculture and food production in Uzbekistan:
1. Enhance Agricultural Import Strategies: Given that a 1% increase in the import index positively influences the food production index, the government could consider streamlining agricultural imports, such as seeds, fertilizers, and technology. Importing high-quality agricultural inputs may help boost productivity and ensure food security.
2. Balance Export Policies: Since an increase in export volume is associated with a decrease in the food production index, it is essential to balance the volume of exports with domestic needs. Policies should be put in place to regulate exports, particularly of essential crops, to prevent negative impacts on local food availability and sustainability.
3. Invest in Yield Improvement Programs: The positive effect of a 1% increase in cereal yield on food production suggests that investing in research, technology, and farmer training to increase crop yields can significantly enhance food security. The government should focus on modernizing farming practices and improving access to yield-boosting resources.
4. Develop Supportive Infrastructure for Agriculture: To complement the findings, developing better irrigation systems, storage facilities, and transportation infrastructure can enhance both import efficiency and yield improvements. This infrastructure will support the long-term sustainability of Uzbekistan's agricultural sector and help optimize the effects of the factors identified in the study.
REFERENSES
1. Van Loon M, Vonk W, (2023) Circularity indicators and their relation with nutrient use efficiency in agriculture and food systems. Agricultural Systems, journal homepage: www.elsevier.com/locate/agsy http;//dio.org/ 10.1016/j.fufo.2022.100122.
2. Ram Kumar Singha , Pawan Kumar Joshi, (2022). Indicator based assessment of food security in SAARC nations under the influence of climate change scenarios. Future Foods, journal homepage: www.elsevier.com/locate/fufo.
3. Ramoudane Orou Sannou a,b, , Sabrina Kirschke a,c , Edeltraud Gunther(2023)) Integrating the social perspective into the sustainability assessment of agri-food systems: A review of indicators, https://doi.org/10.1016/j.spc.2023.05.014.
4. Donald, S.; Martin, K. On Open Innovation, Platforms, and Entrepreneurship. Strateg. Entrep. J. 2018, 12, 354-368. 32. Matteo, R.; Jamel, C.; Domenico, G.; Giuseppe, F. Corporate venture capitalists as entrepreneurial knowledge accelerators in global innovation ecosystems. J. Bus. Res. 2022, 142, 512-523. 33.
5. Dattee, B.; Alexy, O.; Autio, E. Maneuvering in Poor Visibility: How Firms Play the Ecosystem Game when Uncertainty is High. Acad. Manag. J. 2018, 61, 466-498. [CrossRef]
6. Mukhopadhyay, B.R.; Mukhopadhyay, B.K. 'Come Together' with Open Innovation, Research Gate, February 2022. Available online: https://www.researchgate.net/publication/358769735_\T1\textquoterightCome_Togethe r\T1\textquoteright_with_ Open_Innovation (accessed on 20 February 2022).
7. Carvalho, F.; Bonfim, L.; Cruz, A. The process of opening innovation networks: Open innovation at EmbrapaFlorestas. Innov. Manag. Rev. 2021. [CrossRef].
8. https: //www. worldbank. org/en/country/uzbekistan
9. https://www.fao.org/climate-change/resources/publications/en/