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.

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.