Energy used for tillage is an input with a high impact on the cost of crop production; it is desirable to till the soil using minimum energy. The objective of this study was to compare the specific energy expenditure, effect on soil bulk density, and forage yield of maize, by three tillage systems: Disk plow/Disk Harrow/planter (DDP), Chisel plow/Disk harrow/planter (CHDP) and No-Tillage (NT). Energy was measured for tillage operations in the summer season of 2013, 2016, and 2017. Bulk density in 2013 and 2016. Yield in 2013 and 2014. The variables of drawbar force (kN), working speed (m s⁻¹), width (m), depth (m), fuel consumption (L ha⁻¹), bulk density (g cm⁻³), and dry matter yield (Mg ha⁻¹) were measured. Results showed that there were significant differences in the amount of energy used per ha; DDP used an average of 379.75 MJ, CHDP 135.01 MJ, and NT 26.43 MJ. The average energy applied to the soil mass for each system was 400 J kg⁻¹ for DDP, 255.13 J kg⁻¹ for CHDP, and for NT was 237.8 J kg⁻¹. The overall energy efficiency was; 18.23% for DDP, 6.88% for CHDP, and 4.77% for N. The bulk density decreased significantly after three years for NT. There were no significant differences in dry matter yield. In the semiarid condition of Mexico, CHDP and NT are options for saving from 64% to 93% of energy, compared with DDP.

This chapter explores tractor technologies equipped with automatic steering and land management systems using software to generate guidance lines, transforming precision agriculture. Advances in automatic guidance systems for agricultural machinery are analyzed, with a focus on Global Navigation Satellite System (GNSS) and Real-Time Kinematic (RTK)-based guidance systems that enable precise, efficient, and sustainable field operations. The chapter reviews the general characteristics of these technologies and presents notable examples, including John Deere’s AutoTrac system, which leads the commercial adoption of autonomous tractors and automated guidance. Additionally, the integration of these technologies with unmanned aerial vehicles (UAVs) and other monitoring platforms is examined to optimize crop management, reduce input use, and minimize environmental impact. Case studies, operational benefits, technical challenges, and future perspectives in smart agriculture are also discussed.

A variable seed rate system was integrated and evaluated using Trimble technology. Laboratory tests were conducted to assess system performance on a seeder test bench, with seed delivery rates ranging from 180 to 800 seeds per minute. The system was tested using two corn seed plates: a 30-cell Precision Planting plate (PP) and a 40-cell John Deere (JD) plate. The laboratory results showed a cell fill rate of 98.6% for the PP plate and 104.5% for the JD plate. Field performance was evaluated at four sowing densities (100,000; 80,000; 60,000; and 40,000 seeds·ha ⁻¹ ) and three travel speeds (6, 8, and 10 km·h ⁻¹ ). Results showed no significant differences in dosing uniformity due to sowing density otr speed. The system achieved 100% dose uniformity with a coefficient of variation (CV) of just 1.36%, demonstrating high operational reliability. Regarding seed delivery uniformity, an analysis of variance of seed fall timing revealed a significant difference between the plates. The PP plate outperformed the JD plate, with a CV of 10.82% compared to 14.387%, respectively. The system‘s response time to changes in seeding density ranged from 43 milliseconds (from 100,000 to 80,000 seeds·ha ⁻¹ at 10 km·h ⁻¹ ) to 232 milliseconds (from 40,000 to 100,000 seeds·ha ⁻¹ at 6 km·h ⁻¹ ). In conclusion, the integrated variable seed rate system demonstrated high operational reliability. However, an average variability of 12.5% in planter performance was observed, highlighting an opportunity for further optimisation and system improvement.