Climate Impact on Egyptian Agriculture: An Efficiency …
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since they are considered a staple food. The inputs includes: (x 1it ) area of the selected
crops in the targeted governorates measured in acres; (x 2it ) is the cost of fertilizers
measured as Egyptian pounds per acre; (x 3it ) is the cost of pesticides measured as
Egyptian pounds per acre; (x 4it ) represent the cost of labor hours per acre; (y it−1 ) is
the lagged production quantity in terms of tons per lagged year. Summary statistics
for the variables by groups used in the analysis are presented in Table 2.
The summary statistics show the relative similarity among the four groups in
terms of output, land, livestock and labor, and heterogeneity in Machinery and fertilizer. For the efficiency equations, The dependent variable (u it ) is the technical
inefficiency obtained from stochastic frontier model; (Max T emp it ) is average maximum temperature for the governorates under analysis measured as Celsius degrees;
(H umidit y it ) is the water vapor present in the air and this indicator could have a
reason for potential plant infections; (Solar Rad it ) is the power received per meter
squared from the sun measured as Mega Joules per square meter (MJ/m
2 ).
Results of the estimated stochastic frontier models are presented in Table 3. Results
of the production elasticity estimates indicate that, for all governorates, the cultivated
area has the largest positive effect on the quantity produced. In the case of the rice
model, cultivated land, labor and fertilizers are found to have statistically significant influence; lagged rice production has a negative impact on current production
because of the government policy to minimize water resources consumption. In the
case of the Maize model, cultivated area and fertilizers are found to be relevant. For
the wheat model, the cultivated area and lagged production are the relevant factors
affecting production. Other factors in the three models were not statistically significant because these input factors are subsidized since the government provides cereal
farmers, especially wheat producers, with subsidized inputs quantities. These results
are compatible with [58] that identified these factors of production are the most productive in Egypt’s agriculture sector. For the technical inefficiency equations for the
three models, the climatic indicators were found to be relevant in causing a problem of increasing the technical inefficiency and as a result preventing achieving the
maximum potential of producing these crops.
According to Table 4, TE scores for the three cereal crops in the selected governorates are calculated as an output-oriented measure following [59]. The average
technical efficiency score is for wheat and maize is above 90% in Kafr-Elsheikh,
Beher, and Gharbia, while Dakahlia is more technically efficient in Maize production
only, and this is expected since these governorates are the biggest wheat producers.
The average technical efficiency score is for rice range from 80 to 85% along with the
four governorates yet Dakahlia governorate minimum technical efficiency is 80%,
which is higher than the minimum levels in other governorates. These results indicate
that if these governorates effectively use available resources and at the current technology, it will be able to increase the output by 10% on average. Improving technical
efficiency allows reducing production costs and increases competitiveness, which
can help farmers face changing conditions and unfortunate economic circumstances.
Climatic variables are found to be relevant in affecting the technical efficiency and
this call for adopting new agricultural practices that are adaptive to these climate
induced-events. As in the case of poor climatic circumstances, farm margins may be
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since they are considered a staple food. The inputs includes: (x 1it ) area of the selected
crops in the targeted governorates measured in acres; (x 2it ) is the cost of fertilizers
measured as Egyptian pounds per acre; (x 3it ) is the cost of pesticides measured as
Egyptian pounds per acre; (x 4it ) represent the cost of labor hours per acre; (y it−1 ) is
the lagged production quantity in terms of tons per lagged year. Summary statistics
for the variables by groups used in the analysis are presented in Table 2.
The summary statistics show the relative similarity among the four groups in
terms of output, land, livestock and labor, and heterogeneity in Machinery and fertilizer. For the efficiency equations, The dependent variable (u it ) is the technical
inefficiency obtained from stochastic frontier model; (Max T emp it ) is average maximum temperature for the governorates under analysis measured as Celsius degrees;
(H umidit y it ) is the water vapor present in the air and this indicator could have a
reason for potential plant infections; (Solar Rad it ) is the power received per meter
squared from the sun measured as Mega Joules per square meter (MJ/m
2 ).
Results of the estimated stochastic frontier models are presented in Table 3. Results
of the production elasticity estimates indicate that, for all governorates, the cultivated
area has the largest positive effect on the quantity produced. In the case of the rice
model, cultivated land, labor and fertilizers are found to have statistically significant influence; lagged rice production has a negative impact on current production
because of the government policy to minimize water resources consumption. In the
case of the Maize model, cultivated area and fertilizers are found to be relevant. For
the wheat model, the cultivated area and lagged production are the relevant factors
affecting production. Other factors in the three models were not statistically significant because these input factors are subsidized since the government provides cereal
farmers, especially wheat producers, with subsidized inputs quantities. These results
are compatible with [58] that identified these factors of production are the most productive in Egypt’s agriculture sector. For the technical inefficiency equations for the
three models, the climatic indicators were found to be relevant in causing a problem of increasing the technical inefficiency and as a result preventing achieving the
maximum potential of producing these crops.
According to Table 4, TE scores for the three cereal crops in the selected governorates are calculated as an output-oriented measure following [59]. The average
technical efficiency score is for wheat and maize is above 90% in Kafr-Elsheikh,
Beher, and Gharbia, while Dakahlia is more technically efficient in Maize production
only, and this is expected since these governorates are the biggest wheat producers.
The average technical efficiency score is for rice range from 80 to 85% along with the
four governorates yet Dakahlia governorate minimum technical efficiency is 80%,
which is higher than the minimum levels in other governorates. These results indicate
that if these governorates effectively use available resources and at the current technology, it will be able to increase the output by 10% on average. Improving technical
efficiency allows reducing production costs and increases competitiveness, which
can help farmers face changing conditions and unfortunate economic circumstances.
Climatic variables are found to be relevant in affecting the technical efficiency and
this call for adopting new agricultural practices that are adaptive to these climate
induced-events. As in the case of poor climatic circumstances, farm margins may be