Authors

Abstract

Smallholder mechanization in Ethiopia remains low and operationally inefficient, constraining productivity and food security. This study develops and tests a data-driven framework that integrates GIS, Tabu Search, and agronomic scheduling to optimize machinery utilization for the Hitosa Farmers’ Cooperative Union. A mixed-methods design combined a full census of the union’s fleet, GPS field tracking, climate and land-use GIS analysis, CROPWAT-based crop calendars, and time-motion measurements. Optimization was performed using Tabu Search and integer/nonlinear programming implemented in MATLAB and ArcGIS to minimize total travel distance, balance workload, and reduce total operational cost. Spatially differentiated scheduling and route optimization reduced machinery idle time by 30% and produced average fuel savings of 11.28% (23.34% in Hetosa Woreda). According to the assessment, there were 1,230–1,300 hours of inefficient travel and 123,852 ETB in direct labor losses; the estimated revenue losses from combined harvesting and foregone tillage were 2.16 million ETB and 3.33 million ETB, respectively. According to a geospatial demand analysis, 117 tractors (130 HP) or 94 tractors (150 HP) were needed for seedbed preparation, and 137 combine harvesters (220 HP) were estimated to be needed throughout the union's service area. While prioritizing equipment allocation based on crop timeliness and terrain, the updated scheduling and routing decreased the overall distance variance between machines. Cooperative mechanization yields quantifiable economic and environmental benefits when GIS, optimization algorithms, and agronomic calendars are integrated. GPS-enabled devices, centralized logistics, and scheduled fleet replacement are among the priorities; cooperative systems can be compared using the suggested framework to improve service sustainability, dependability, and cost-effectiveness.

Abstract in Afrikaans

Mekaanizeeshiinii qonnaan bultoota xixiqqaa Itoophiyaa keessatti yeroo ammaa bu’a qabeessa miti, kunis oomisha fi nageenya nyaataa irratti dhiibbaa guddaa fida. Qorannoon kun haala Waldaa Qonnaan Bultoota Hitosa irratti xiyyeeffachuun, GIS, Tabu Search, fi tarsimoo qonnaa walitti makuun meeshaalee qonnaa itti fayyadamuu fooyyessuuf qophaa’e. Qorannoon kun mala qorannoo walmakaa fayyadamuun, lakkoofsa guutuu meeshaalee waldaa, hordoffii lafa irratti GPS, qorannoo qilleensa fi fayyadama lafa (GIS analysis), karoora oomisha (CROPWAT), fi safartuu hojii (time-motion) of keessatti qabata. Fooyya’insi kun Tabu Search fi integer/nonlinear programming MATLAB fi ArcGIS irratti hojiirra oolee, kaayyoon isaa fageenya imalaa xiqqeessuu, ulfina hojii walqixa qooduu fi baasii hojii guutuu hir’isuu ture. Tarsimoon haaraan yeroo meeshaaleen hin hojjenne dhibbeentaa 30% hir’ise, akkasumas balaqqee boba’aa giddu galeessa 11.28% (gara 23.34% tti Hetosa keessatti) akka ta’e mirkaneesse. Qorannoon akka agarsiisutti, sa’aatii hojii bu’a qabeessa hin taane 1,230–1,300 fi miidhaan mindaa hojii to’ataa qarshii 123,852 ETB dha. Dabalataan, badhaadhinni dhabame oomisha walitti qabuu irraa 2.16 miliyoona ETB fi qotannoo dhabameen 3.33 miliyoona ETB ta’uu ittiin tilmaamame. Qorannoon geospatial agarsiise traktorii 117 (130 HP) yookaan 94 (150 HP) fi combine harvester 137 (220 HP) guutuu daangaa tajaajila waldaa keessatti barbaachisu akka ta’e. Tarsimoon haaraan oomisha yeroo fi haala lafa irratti hundaa’ee meeshaalee qooduun garaagarummaa imalaa xiqqeesse. Waliigaltee GIS, algoritiimii fooyya’insaa fi karoora qonnaa waliin fayyadamuun bu’aa dinagdee fi naannoo argamsiisa. Kana malees, fayyadama GPS, tajaajila geejjibaa walitti qabamee fi fleet yeroo murtaa’eetti jijjiiruu dursa kennee, tajaajila itti fufaa, amanamaa fi baasii gadi bu’e mirkaneessa.