Selecting the appropriate tractor for small-scale farms is a complex process due to the multitude of technical, environmental, and economic criteria that must be evaluated. This study addresses this challenge by integrating the Analytic Hierarchy Process (AHP) with machine learning (ML) to reduce the number of criteria and simplify the decision-making process. The research aims to determine the most relevant criteria aligned with sustainable development goals for selecting the right tractor, focusing on small farms in the Egyptian Delta. Four tractors, with horsepower ranging from 55 to 95, were evaluated based on inputs from forty-two governmental service providers in the study area. Initially, nine criteria were identified, encompassing key technical, environmental, and economic factors. These criteria were reduced to three—price, power, and maintenance costs by weights 0.142, 0.334, and 0.525, respectively—using Hierarchical Agglomerative Clustering with Euclidean distance. This reduction streamlined the selection process, making it more practical for farmers. Results show that the second tractor (T2), with a priority score of 0.326 and a normalized value of 33.4%, emerged as the optimal choice for small-scale farmers, outperforming the first tractor (T1) (28.7%) and third tractor (T3) (21%). Integrating AHP and ML simplifies tractor selection, ensuring sustainability, cost-efficiency, and operational effectiveness.

Agricultural production can achieve sustainability by appropriately applying agricultural mechanization, especially in developing countries where smallholding farmers lack sufficient agricultural machinery for their farming operations. This paper aimed to study the extent to which small-, medium-, and large-scale farms in the Delta of Egypt use agricultural mechanization in their wheat crop farming operations. K-means clustering was used to aggregate and analyze the scenarios implemented by farmers for wheat cultivation so as to suggest guidelines for each cluster of farmers on how to mechanize their indoor wheat agricultural operations to maximize production. The study is divided into two parts: Firstly, data were collected regarding the percentage of small, medium, and large farms; the cultivated area of wheat crops in small-, medium-, and large-scale farms; and the size of tractors, as an indicator of the mechanization available in the governorates of Egypt’s Delta. Secondly, data were collected through a questionnaire survey of 2652 smallholding farmers, 328 medium-holding farmers, and 354 large-holding farmers from Egypt’s Delta governorates. Based on the surveyed data, 14, 14, and 12 scenarios (indexes) were established for small-, medium-, and large-scale farms, respectively, related to various agricultural operations involved in wheat crop production. These scenarios were analyzed based on the centroids using K-means clustering. The identified scenarios were divided into three clusters for the three levels of farms. The data obtained showed the need for smallholding farmers to implement mechanization, which could be achieved through renting services. These findings, if implemented, would have huge social and economic effects on farmers’ lives, in addition to increasing production, saving time and effort, and reducing dependence on labor.

Smallholding farms spread throughout the world, and they consider the cornerstone of agriculture production in Africa and Asia. Smallholder farms produce over 80 % of the food used in most of the developing countries. However, smallholder farmers face many problems, including the lack of appropriate mechanization (AM), labor shortage, increase wages, climate change, and poor access to modern inputs, credit, and markets. The paper focuses on analyzing and proposing the criteria to select and design AM for smallholder farmers through the linkage between farm size, machine scale, and productivity. The machine designers and manufacturers should take some environmental and social implications and machinery support services into consideration to deliver AM to smallholder. The adoption of AM with appropriate planting systems such as CA will ensure sustainability in the production of smallholder farmers. The adherence to these standards with overcoming smallholding challenges will lead to growth and sustainability for smallholder farmers.

This study investigates the impact of various pre-drying treatments and drying methods on the physicochemical, phytochemical, and sensorial quality of dried bananas. To achieve this goal, four pretreatment solutions were used before drying to enhance the quality characteristics of banana slices: citric acid 1% and ascorbic acid 1%, sucrose 10%, sodium bisulphate 0.5%, and lemon juice 5% and acetic acid 0.5%. Two drying methods were applied, sun drying (SD) and tunnel drying (TD). The dried slices were evaluated for moisture, total acidity, water activity (a_w), browning index, rehydration ratio (RR), reducing sugar, ascorbic acid, total phenol, and DPPH %, as well as sensory attributes (color, odor, taste, texture, and overall acceptability). The results showed that the retention of acid content, ascorbic acid, total phenolics, and DPPH in slice samples dried in TD was higher than that in SD. The browning index values in the TD samples were lower than those in the SD samples. Moreover, the TD samples showed a higher RR than the SD samples. It is noted that the pretreatment solutions used have an important role in improving the final quality characteristics of dried banana slices. The results of the sensory evaluation revealed a significant difference in the degrees of color and texture for the slices pretreated with sodium bisulphate. Meanwhile, the rates of taste and odor were significantly higher for the sucrose-pretreated slices. TD can improve the drying kinetics, shorten the drying time, and maintain the quality of the dried slices.

Assessing the energy cycle and greenhouse gas (GHG) emissions of wheat production in small Egyptian farms is essential to improve wheat productivity to meet population growth and achieve sustainable development. This study aims to compare wheat production in terms of energy use and GHG emissions for different scenarios in the Delta of Egypt and to use Data Envelopment Analysis (DEA) to optimize the wheat production system. Three common scenarios of the wheat production system (S-I, S-II, and S-III) from old lands with one scenario (S-IV) from newly reclaimed land were included in the study. Data were collected from small farmers through a face-to-face questionnaire and interviews in 2022–2023. The results showed that the third scenario (S-III) in the old lands had the lowest input energy consumption (42,555 MJ ha⁻¹) and the highest output energy (160,418 MJ ha⁻¹), with an energy use efficiency of 3.770. In comparison, the input and output energy for the newly reclaimed scenario (S-IV) were 37,575 and 130,581 MJ ha⁻¹, respectively, with an energy use efficiency of 3.475. S-III was an optimum scenario due to its high energy indicators, such as energy productivity of 0.173 kg MJ⁻¹. The total GHG emissions of S-III were the lowest in old lands with a value of 1432.9 kg CO_2-eq ha⁻¹, while S-IV had 1290.2 kg CO_2-eq ha⁻¹. The highest GHG emissions input was diesel fuel for machinery and irrigation, followed by manure, chemical fertilizers, and agricultural machinery use. Using mechanization in most farming operations for S-III and S-IV led to decreased losses of agricultural inputs with increasing outputs (yield and straw). Therefore, using them in wheat farming practices is recommended to increase the wheat farming system’s energy efficiency and GHG emissions.

The introduction of ridge tillage and beans as a cover crop was investigated as a means of management for mitigating soil erosion and increasing the physical functionalities of soil. In a completely randomized design, four treatments were applied to twelve plots, three with ridges introduced (T1), three with beans as the cover crop (T2), three with cover crops combined with ridges (T3), and another three as controls without intervention (T4). Four physical properties were monitored, i.e., soil moisture content, bulk density, infiltration rate, and aggregate stability. Data were collected from two seasons with beans as the cover crop, with each season lasting three months. The results showed that T3 resulted in the highest soil moisture content at 34.87 ± 6.78%, followed by T2 and T1 with 34.20 ± 0.65% and 32.65 ± 1.71%, respectively, while T4 had the minimum value of 28.28 ± 5.30%. The bulk density of the soil was found to be lowest at T2 and T3, both having a value of 0.92 g cm⁻³ with standard deviations of ±0.03 and ±0.11, respectively. This was followed by T1 with 0.98 ± 0.05 g cm⁻³ while T4 had the highest bulk density of 1.17 ± 0.13 g cm⁻³. A similar trend was observed for both the basic infiltration rate and aggregate stability, except that, in terms of the latter, T1 was ranking second after T3, with 64.07% of water stable aggregates greater than 0.25 mm diameter. The interventions introduced in coffee plantations had significant effects on the bulk density and infiltration rate; however, there was no significant difference in the moisture content and aggregate stability. Further investigation is needed to quantify the environmental effects of these interventions, e.g., greenhouse gas emissions and yields.