Agricultural machinery distribution programs are key components of the Philippine government to strengthen its economic productivity. However, concerns were reported that some of the distributed machines were under-utilized or unutilized in their respective farms. This study is focused on the development of a GIS-based model of determining suitable lowland rice areas for two-wheel and four-wheel tractors. A numerical rating system was designed that assigns a suitability score from three criteria, namely slope, road network proximity, and flood risk, based on established scales. Analytical Hierarchy Process was used as an approach to determine the relative influence in which results show that slope has the greatest weight (65%) followed by flood risk and road network proximity with 20% and 15%, respectively. The total suitability score (TSS) is then determined by getting the weighted average of the individual suitability scores. Land suitability mapping on the study area, the CALABARZON region, show that majority of the lowland rice areas are highly suitable for two-wheel and four-wheel tractors (81.39%) while some areas fall under marginal (15.03%) and moderate suitability (3.58%). Geotagged locations of the distributed agricultural tractors from 2015 to 2020 reveal that 78% of the distributed two-wheel tractors and 80% of four-wheel tractors in the region are situated in highly suitable areas. The GIS-based suitability model generated in this study can be utilized by the government to improve its machine distribution programs of two-wheel and four-wheel tractors in lowland rice farms.

Peanut harvesting remains labor-intensive and costly due to the limited availability of locally developed mechanized harvesting technologies. Thus, this study presents the design, optimization, and evaluation of a four-wheel tractor peanut digger to advance harvesting. Structural integrity was evaluated using Finite Element Analysis (FEA) in ANSYS under a draft load of 2008.2 N, yielding a maximum von Mises stress of 33.012 MPa, a peak strain of 0.00015, a maximum deformation of 1.72 mm, and a factor of safety of 5.74, confirming adequate structural reliability. Field performance was optimized using a Box–Behnken Design (BBD) with tractor speed, digger blade lift angle, and clearance angle as factors, while material capacity and digging efficiency served as response variables. Regression analysis showed that the main effects significantly influenced both responses, whereas interaction effects were insignificant. Response Surface Methodology (RSM) optimization identified optimal operating conditions of a 14.95° lift angle, 15.70° clearance angle, and 1.74 km·h⁻¹ forward speed, achieving a predicted material capacity of 0.56 t·h⁻¹ and digging efficiency of 96.69%. Validation tests improved these values to 0.59 t·h⁻¹ and 97.42%, demonstrating that integrating numerical methods provides an effective approach for developing adoptable and structurally reliable peanut harvesting machinery.