This research tested the impact on the soil when implementing a Clean Intensive Livestock Production System (CILPS) via Voisin Rational Grazing (VRG). The methodology consists of the characterization of soil-grass-cattle management, including the soil chemical and biological analysis in the case study farm and reference land (comparison between extensive and CILPS-VRG systems). In addition, the VRG impact on agroecological management was evaluated by calculating the Soil Organic Matter Indicator (SOMI). Once the samples were processed, the T-Student test was applied, verifying the homogeneity of variances (F-Fischer) for the chemical variables and the Kruskal-Wallis test for the biological variables. The results identified substantial differences in some edaphic parameters such as organic matter, percentage of organic carbon, nitrogen, sulfur, and calcium. Moreover, to diversify nematodes and differences in the Rhabdhitids sp., the process characterized products from the natural organic fertilization in the soil with VRG. Finally, the calculation of the SOMI with a value of 41.6 for VRG indicates soil with no alteration or natural balance.

In Colombia, agricultural exports have become notoriously prevalent in recent years, causing the creation of new methods capable of increasing production in order to meet the global demands. A very efficient option is the use of greenhouses, given their low building cost, ease of construction, ability to protect crops from natural phenomena and plagues, and the possibility to keep the internal temperature steady during day and night, thus allowing crops to grow fast and healthy. Nowadays, advancements in electronics have allowed boosting the positive effects of these environments, which is why this document introduces a procedure for the implementation of an automated pyramid-type greenhouse, utilizing techniques related to Precision Agriculture (PA) and based on concepts related to the Internet of Things (IoT) for remote monitoring through emerging communication technologies such as the NFRL2401 cards and the Arduino Nano and Mega boards. Inside the greenhouse, variables such as temperature and ambient humidity are measured and controlled via the PCE-P30U Universal Input Signal Converter Data Logger, while ground humidity is monitored by ZD510 capacitive sensors. Outside, variables such as temperature, ambient humidity, negative and positive pressure, and wind speed are measured. Data obtained is taken wirelessly to the server using Windows Server 2019 Datacenter, with Broker MQTT EMQ-X services and MYSQL databases, providing a suitable and efficient environment for agricultural research processes. With the procedure developed in this document, a baseline is proposed for the implementation of a smart greenhouse that can be replicated and used as a test system for smart sowing processes, adapting to the different climate and production conditions of the country.