With 5 millions heads, goats occupy the second place after sheep in Morocco. The indigenous populations are generally raised for meat, except Draa goat that is raised as a dual purpose doe. The objective of this study was to assess milk yield and composition of Draa indigenous goat breed under an intensive system of three kiddings in 2 years and to evaluate the effects of non-genetic factors. Data were collected on 381 lactations between 2006 and 2012 and on 174 samples for milk composition between 2008 and 2010. The highest milk yield was recorded in does of 36-48 months old (84.3 ± 4.78), those suckling more than one kid (80.0 ± 3.39) and those kidding in spring (80.4 ± 3.60) and summer (79.9 ± 3.67). Except protein content, milk composition was not affected by age of does. Dry matter and fat content increased significantly with the lactation stage, protein content decreased from early to middle/late lactation, and lactose content was high in middle lactation. The highest dry matter and fat and lactose contents were obtained for summer kiddings, while the highest percent of protein was recorded for autumnal kiddings. It was concluded that under the three kiddings in 2-year system, Draa does produce enough milk with an interesting milk composition, indicating that this system may be used with success to improve farmers' income.

Background. In the mountain areas of south-east Morocco, farmers are engaged in the solar drying of figs using traditional processes. However, this practice leads to losses in quality. This study aims to improve this drying method by designing and studying the performance of a natural convection solar dryer and the quality of fresh and dried figs. Materials and methods. An indirect solar dryer was designed and its performance was studied. The TSS content, moisture, firmness and morphological parameters of fresh and dried figs were determinated. Results. The average difference (inside/outside) in the temperature of the dryer is +8.3°C. This will allow a decrease in the mean drying duration from 10 days (traditional drying) to 4 days. This duration is significantly correlated with air humidity (R 2 = 0.84) and temperature (R 2 = 0.63). The relationship between the degree of dehydration (%) and time follows a polynomial model with a determination coefficient of 0.98. Fig-drying yield is 38.7% and dried figs have a high (TSS) content with 63.7% and a firmness of 6.03 kg/cm 2 . The water content was approximately 25.6%. Conclusion. The solar dryer with natural convection designed in this study can be an alternative to traditional drying practised by small farmers in the mountainous regions of Errachidia. . (2018). Design of a solar dryer for small-farm level use and studying fig quality. Acta Sci. Pol. Technol. Aliment., 17(4), 359-365. http://dx.doi.org/10.17306/J.AFS.2018.0599 360 www.food.actapol.net/and use of renewable and free energy (sun), produces good-quality dried products compared to traditional solar drying and enables the duration of solar drying to be reduced.

Background. In the mountain areas of south-east Morocco, farmers are engaged in the solar drying of figs using traditional processes. However, this practice leads to losses in quality. This study aims to improve this drying method by designing and studying the performance of a natural convection solar dryer and the quality of fresh and dried figs. Materials and methods. An indirect solar dryer was designed and its performance was studied. The TSS content, moisture, firmness and morphological parameters of fresh and dried figs were determinated. Results. The average difference (inside/outside) in the temperature of the dryer is +8.3°C. This will allow a decrease in the mean drying duration from 10 days (traditional drying) to 4 days. This duration is significantly correlated with air humidity (R 2 = 0.84) and temperature (R 2 = 0.63). The relationship between the degree of dehydration (%) and time follows a polynomial model with a determination coefficient of 0.98. Fig-drying yield is 38.7% and dried figs have a high (TSS) content with 63.7% and a firmness of 6.03 kg/cm 2 . The water content was approximately 25.6%. Conclusion.The solar dryer with natural convection designed in this study can be an alternative to traditional drying practised by small farmers in the mountainous regions of Errachidia.

An in-depth determination of date fruit properties belonging to a given variety can have an impact on their consumption, processing, and storage. The objective of this study was to characterize date fruits of the ‘Mejhoul’ variety using (i) objective and non-destructive image-analysis features and (ii) measurements of physicochemical parameters. Based on images acquired using a digital camera, more than 1600 texture parameters from the individual color channels L, a, b, R, G, B, X, Y, and Z, and 40 geometric characteristics (including linear dimensions and shape factors for each fruit), were determined. Additionally, pomological features, water content, water activity, color parameters (L*, a*, b*), total soluble solids (TSS), reducing sugars, and total sugars were measured. As a main result, the application of machine vision allowed for the correct detection of ‘Mejhoul’ dates and the determination of the image features. The differences in the values of the histogram’s mean (HMean texture) for individual color channels were determined. The ‘Mejhoul’ date fruit images in color channel a (aHMean equal to 145.88) and color channel b (bHMean: 145.49) were the brightest, and in channel Z they were the darkest (ZHMean: 4.23). Due to the determination of the elliptic shape factor (W1) of 1.000 and the circular shape factor (W2) of 0.110, the elliptical shape of the fruit was confirmed. On the other hand, ‘Mejhoul’ dates were characterized by a length of 47.3 mm, a diameter of 26.4 mm, flesh thickness of 6.25 mm, total soluble solids of 62.1%, water content of 28.0%, water activity of 0.652, hardness of 694 g, reducing sugars of 13.8%, and total sugars of 58.8%. Due to the determination of many image features and other parameters, this paper presents the first comprehensive characterization of ‘Mejhoul’ date fruits using a non-destructive imaging technique linked to some physicochemical quality attributes.

The aim of this study was to develop the procedure for the varietal discrimination of date palm fruit using image analysis and traditional machine learning techniques. The fruit images of ‘Mejhoul’, ‘Boufeggous’, ‘Aziza’, ‘Assiane’, and ‘Bousthammi’ date varieties, converted to individual color channels, were processed to extract the texture parameters. After performing the attribute selection, the textures were used to build models intended for the discrimination of different varieties of date palm fruit using machine learning algorithms from Functions, Bayes, Lazy, Meta, and Trees groups. Models were developed for combining image textures selected from a set of all color channels and for sets of textures selected for individual color spaces and color channels. The models, including combined textures selected from all color channels, distinguished all five varieties with an average accuracy reaching 98%, and ‘Bousthammi’ and ‘Mejhoul’ were completely correctly discriminated for the SMO (Functions) and IBk (Lazy) machine learning algorithms. By reducing the number of varieties, the correctness of the date palm fruit classification increased. The models developed for the three most different date palm fruit varieties ‘Boufeggous’, ‘Bousthammi’, and ‘Mejhoul’ revealed an average discrimination accuracy of 100% for each algorithm used (SMO, Naive Bayes (Bayes), IBk, LogitBoost (Meta), and LMT (Trees)). In the case of individual color spaces and channels, the accuracies were lower, reaching 97.3% for color space RGB and SMO and LMT algorithms for all five varieties and 99.63% for Naive Bayes and IBk for the ‘Boufeggous’, ‘Bousthammi’, and ‘Mejhoul’ date palm fruits. The results can be used in practice to develop vision systems for sorting and distinguishing the varieties of date palm fruit to authenticate the variety of the fruit intended for further processing.