The objective of this study was to investigate the use of four different mathematical functions (Wood, Inverse Polynomial, Quadratic and Cubic models) for describing the lactation curve of unimproved Awassi ewes. Data were collected from 136 ewes from the same flock raised on the State Farm of Gözlü in the Konya Province of Turkey. The differences in estimated total milk yields between the models were not statistically significant. All models were adequate in describing total milk yield, though total milk yield estimated using the Cubic model was very close to total milk yield calculated by the Fleischmann method. Age effects on model parameters were not significant. The Inverse Polynomial model overestimated the peak yield significantly. Estimated peak yields of the Wood and Cubic model were similar while that obtained from the Quadratic model was significantly lower than that of the other models. Day of peak yield estimated by the models varied between 10.2 and 56.4 days. The differences between days of peak yield estimated using the different models were significant. R 2 values of the models ranged from 0.724 to 0.977. The Cubic model gave the best R 2 value. The lowest mean square prediction error was found using the Cubic model. Correlation coefficients between total milk yield calculated by the Fleischmann method and estimated total milk yield from the other models ranged from 0.933 to 0.998. The highest correlation coefficient was found for the Cubic model. As a result, the Cubic model showed the best fit to the data collected from unimproved Awassi ewes and allowed a suitable description of the shape of the lactation curve.

An ideal pesticide should be toxic only to the target organism and biodegradable, and its residue should not affect nontarget surfaces (Chowdhury et al., 2008). One such ideal alternative is the use of natural plant products that have pesticidal activity, such as azadirachtin (Akça et al., 2005). Azadirachtin possesses insecticidal activity against many economically important insect pests such as Helicoverpa armigera, Spodoptera litura, Plutella xylostella, Sitophilus oryzae, Sitophilus zeamis, Earis vitella, Aphis gossypii, Bemicia tabaci, and Pectiniphora gossypiella, and nematodes like Cosmopilitis sordidus. The belief that such natural insecticides are safe or less damaging to the ecosystem also needs to be further validated, as their effect on nontarget organisms is reportedly very close to threshold chronic toxicity (Schmutterer and Singh, 2002;Gopal et al., 2007).

No abstract

The experience shows that when constructing soil fertility models, many researchers prefer single-valued regression analysis. This is primarily due to the fact that regression analyses require simpler statistical calculations, and on the other hand, regression equations enable a physical explanation of the process under study. The research goal is to determine the effect of soil fertility indices and mineral fertilizers on the yields of crops (cereals) grown in the Karabakh Steppe on gray-brown irrigated soils.

Artificial intelligence systems are widely accepted as a technology providing an alternative method to solve complex and ill-defined problems. Artificial neural network (ANN) is a technique with a flexible mathematical structure, which is capable of identifying a complex nonlinear relationship between the input and output data. The objective of this study was to investigate the relationship between dust concentration and wind erosion rate, and to illustrate how ANN might play an important role in the prediction of wind erosion rate. Data were recorded via field experiments by using a portable field wind tunnel. The experiments were carried out for eight different tillage applications that include the conventional, six different reduced tillage and the direct seeding practices. Particulate matter (PM) concentration generally decreased with a decrease in number or intensity of tillage operations. Direct seeding resulted in the lowest PM 10 concentration. After tillage applications, wind erosion rate varied between 113 and 1365 g m -2 h -1 . Results showed that wind erosion rate was lower in direct seeding than in conventional and reduced tillage applications. In this paper, a sophisticated intelligent model, based on a 1-(8-5)-1 ANN model with a back-propagation learning algorithm, was developed to predict the changes in the wind erosion rate due to dust concentration occurring during tillage. In addition, the prediction of the model was made according to traditional methods of wind erosion rate by using the programme Statistica, version 5. The verification of the proposed model was carried out by applying various numerical error criteria. The ANN model consistently provided better predictions compared with the nonlinear regression-based model. The relative error of the predicted values was found to be less than the acceptable limits (10%). Based on the results of this study, ANN appears to be a promising technique for predicting wind erosion rate.