The Libyan Journal of Agriculture

Monitoring an Onion Crop Using the MISBAR Platform

Shaban Munsur — 2025-01-15

The study was conducted with the aim of monitoring the change in land cover of Al Jabal Al Akhdar region in the northeastern part of Libya during the period between 1994 – 2023. After collecting, pre-processing, and preforming supervised classification of Landsat satellite imagery which covers 1204365 hectares, the study area was classified into four classes: Urban Areas, Barren Lands, High Density Vegetation (forest), and Low Density Vegetation. The results show that the High Density Vegetation was covering 288971 hectares in 1994 and decreased by 131017 hectares in 2023. The rate of change in this category during the study period reached 45.3%, with an annual rate of change of 5.7%. The results also showed that this decrease was accompanied by a significant increase in Urban Areas, Low Density Vegetation, and the Barren Lands by 6691, 82481, 48154 hectares, respectively. This represents an increase of 76.4%, 17.7%, and 9.5%, respectively. The change in the average annual rate for these classes was 230.7, 2844.2, and 1442.9 ha/year, respectively. The severe decline of this natural forest cover may lead to the extinction of many plant and animal species, especially endemic ones, and further degradation of the ecosystem. The observed deterioration of the forest should prompt an urgent action by activating laws related to forest protection and achieving sustainable development to meet the local population needs and working towards repairing the degraded areas through ecological restoration activities.

Developing pedotransfer Models Using Random Forest and Regression and Multiple Linear Regression to Estimate Field Capacity and Permanent Wilting Point for Wadi Al-Hai Soils, Libya

Younes Daw Z. Ezlit — 2025-01-15

This study aimed to develop and evaluate the performance of a transformational predictive model using Random Forest Regression (RFR), a machine learning tool, and compare it with a model derived from Multiple Linear Regression (MLR). The objective was to estimate the field capacity (FC) and permanent wilting point (PWP) of soils using surface samples collected from 157 representative soil profiles in Wadi Al-Hai, Al-Jafara Plain, Libya. These samples were obtained from the Hidroprojekat report (1974). the input variables for model development included the percentages of sand, silt, and clay, as well as bulk density, particle density, and organic carbon content. The performance of the MLR-derived models varied depending on the input variables and the soil property being predicted. The best MLR model for predicting field capacity was obtained when sand, silt, and clay were used as inputs, with MAE, RMSE, R², and Dash (C²) values of 2.32%, 2.89%, 0.84, and 0.77, respectively. For the prediction of the permanent wilting point, the best MLR model included sand, silt, clay, bulk density, particle density, organic carbon, and measured field capacity as inputs, resulting in MAE, RMSE, R², and Dash (C²) values of 1.78%, 2.23%, 0.56, and 0.56, respectively. However, the overall predictive performance of the MLR models was inadequate for reliable estimation of field capacity and permanent wilting point. When the RFR method was employed to develop predictive models for both properties, it outperformed the MLR models significantly. The RFR-derived model for field capacity yielded superior results, with MAE, RMSE, R², and Dash (C) values ranging from 0.88–1.15%, 1.14–1.24%, 0.93–0.95, and 0.93–0.99, respectively. Similarly, the RFR model demonstrated higher predictive accuracy for the permanent wilting point, achieving MAE, RMSE, R², and Dash (C²) values between 0.61–0.88%, 0.68–1.43%, 0.93–0.95, and 0.93–0.99, respectively. Notably, the predictive accuracy of all models improved as the number of input variables increased.to validate the applicability of these models within a Geographic Information Systems (GIS) environment, the best RFR-derived model was used to produce spatial prediction maps via the Inverse Distance Weighted (IDW) method. The generated maps closely aligned with the spatial distribution maps of the measured values. This study recommends further exploration and application of machine learning models, particularly Random Forest Regression, for predicting soil properties that are challenging to measure directly.

Study of the tractor and agricultural machinery market and its impact on the automation of agricultural operations

Farj Ali Jbail — 2025-01-15

Automating agricultural processes is the best way to improve and increase agricultural production in terms of quantity and quality The tractor and agricultural machinery market should work on introducing agricultural automation for sustainable development in the agricultural sector, The study aims to define agricultural automation and its importance in the application of automated agriculture, In addition to knowing the extent to which the tractor and agricultural machinery market keeps pace with achieving comprehensive automation of all agricultural operations, The study was conducted through a questionnaire prepared for this purpose and distributed to companies and shops importing and selling agricultural machinery and equipment, The results indicated that the growth witnessed by the agricultural machinery and equipment market in the study area was limited at 58%, 75% of the sample indicated that the demand rate for agricultural machinery and equipment in the market was averageThere is a need to introduce new products in the market 94%, and 50% have no difficulties in marketing, and 44% have no problems meeting market demand. Digital marketing via the Internet is 34% ahead of other advertising methods, It was found that 77% of the financing of agricultural companies is private financing from the owners of these companies. One of the biggest challenges and difficulties facing agricultural companies is the lack of investment in the agricultural sector at 38%, which requires providing easy financing for companies and farmers at 37%, in addition to training for employees and workers in agricultural companies and workers in the agricultural sector at 32%,The study concluded that the automation of agricultural operations is weak due to the failure of the tractor and agricultural machinery market to achieve automation. This is due to the lack of investment in this field due to limited financing, in addition to the low level of training and qualification and other factors.

Comparison of honeybee queen’s quality from Kufra and Tripoli based on colony performance

Muhammad E. Lamah — 2025-01-15

The western honeybee subspecies Apis mellifera exhibit variations in morphological and biological traits due to geographic factors and natural barriers that isolate different regions. This study aimed to evaluate the quality of honeybee queens from Kufra (an isolated region) and Tripoli (a non-isolated region) by analyzing colony performance during the spring season. Four colonies from each region were selected, each containing seven brood frames fully covered with bees and headed by a one-year-old queen. Colony performance was assessed using several criteria, including hygienic behavior, varroa mite infestation rates, honey production, and aggressiveness level. The results indicated that colonies from both regions demonstrated high levels of hygienic behavior, low varroa infestation rates, and moderate honey production. However, all colonies exhibited a high level of aggressiveness. No significant differences were found in the biological activities of colonies from the two regions, except for aggressiveness. Worker bees from Tripoli displayed more aggressive behavior, pursuing longer distances (P = 0.018) and for extended durations (P = 0.032) compared to those from Kufra. This study also recorded the presence of varroa mite infestations in colonies from Kufra and highlighted the strong similarity between colonies from both regions in all biological activities, except for their level of aggressiveness.

Effect of adding rosemary leaves (Rosmarinus officinalis L.) powder to feed on growth performance and some blood parameters of broiler in hot climate region

Wafa Mohamed Enami — 2025-01-15

The study was conducted at Poultry Research Station of Agriculture Faculty - University of Tripoli, Libya. The aim of this study was to investigate the effect of adding different levels of rosemary leaves powder to broilers` feed on growth performance, carcass quality and some blood parameters. A total of four hundred one day old Ross 308 unsexed broiler chicks were used. The experiment lasted to 42 days. The chicks were divided to five treatment groups according to the percentages of rosemary leaves powder added to feed; 0.00%, 0.25, 0.50, 0.75 and 1.00%. The treatments were distributed on replicates by using complete random design. Each treatment was applied in four replicates with 20 chicks each. The results showed that the birds consumed rosemary leaves powder at 0.25, 0.50, and 0.75 achieved highly significant reduction in averages of daily feed intake, feed conversion ratio and mortality% compared with the birds in control treatment. Moreover, groups on 0.50 or 0.75% rosemary powder have shown a significant reduction in proportion of abdomen lipid layer comparing to control group. On the other hand, the results showed no significant differences between the five groups in body weight, body weight gain, carcass characteristics, parameters, notably, of glucose, cholesterol, T3 and T4.

Utilizing Marker-Assisted Selection (MAS) for Morphological Traits in the F3 Generation of Tomato (Solanum lycopersicum).

Amani Emsaed — 2025-01-15

The use of molecular markers has become a pivotal component of molecular breeding programs in tomato. In the third generation of this research, two lines derived from the crossbreeding of commercial hybrids were subjected to detailed analysis using molecular markers by ten RAPD-based primers. Among these primers, six (OPA01, OPA05, OPA07, OPA08, OPA09, and OPB10) successfully amplified targeted DNA regions. Significantly, primer OPA07 uniquely linked specific traits in the first line to distinct bands of 280 and 180 base pairs. In the second line, all six primers achieved amplification, with four primers (OPA07, OPA08, OPA09, and OPB10) associating selected traits with distinct bands of 450, 500, 350, and 450 base pairs, respectively. These bands are likely to serve as marker-assisted selection (MAS) tools, facilitating the selection of plants with desired phenotypic traits in tomato breeding. These markers are expected to greatly improve marker-assisted selection techniques and potentially reveal novel genetic resources, thus enriching the genetic diversity within tomato breeding programs for future advancements.