Title : PDCA application in bean cultivation in Northern Mexico
Among the five varieties registered in 2010 by the INIFAP network of beans and other grain legumes in Durango is Pinto Bravo, which has been evaluated in different environments to establish its level of tolerance to environmental factors, which reduce productivity and grain quality. Among the factors that reduce bean productivity in the Semi-arid Altiplano, is humidity stress, which is caused by scant and erratic distribution of rain and edaphic conditions, such as sandy, shallow soils, poor in organic matter. and with low moisture retention capacity. (Rosales Serna and Collaborators; 2011).
Climate change is the greatest environmental threat facing humanity, it is the evil of our time and its consequences can be devastating if we do not drastically reduce our dependence on fossil fuels and greenhouse gas emissions. In fact, the impacts of climate change are already perceptible and are evidenced by data such as:
• The global average temperature has already risen 1.1 ° C.
• Damage to crops and food production.
• Extreme meteorological phenomena. (Greenpeace; 2019)
PDCA is a management method that corresponds to the actions necessary to guarantee the solution of a problem. The problem can be good, when it is better than the goal, or bad when it comes to unwanted deviations in a certain pattern. The objective of the PDCA cycle is to guarantee a process of continuous improvement, where the treatment of anomalies is guaranteed, seeking to increase productivity. (Rock Content and collaborators; 2018).
Objective: Apply the PDCA methodology to evaluate the germination efficiency of the various bean plant varieties (Bravo, Centauro and Saltillo), forecasting and eliminating potential risks, with the use of quality tools that allow to identify, measure and try to control the process with the help of statistical analysis using the statistical package MINITAB.
Method: The experiment was developed in the period May-September 2020 in the San Antonio de los Bravos experimental field of the Antonio Narro Autonomous Agrarian University in a geographic location of North Latitude: 250 33´ 21 ”, West Longitude: 1030 22´ 36 In the city of Torreón Coahuila Mexico. Three varieties of beans (Phaseolus vulgaris) were used: Pinto Bravo, Pinto Centauro and Pinto Saltillo by means of a random block design with three repetitions each.
Results: The ANOVA method for a single factor was used to compare seed germination, plant height and stem thickness, validating with the Tukey test with a reliability of 95 percent. Obtaining that there is no significant difference in seed germination, plant height and stem thickness, so the null hypothesis is accepted for these variables. In the first stage, the Plan stage, variables such as sowing technique, dates and irrigation technique, quantity and technique of fertilizer use, quantity and technique in the use of insecticides, plant germination, plant size, thickness, and climate, considering pest infection as a risk for the second stage of Making, the quality tool was used to generate solutions to control the process and thus reduce the possibility of potential risks occurring, in the third stage of Check, favorable results are observed that generate a normalized behavior in the variables that allowed to control the process and for the last stage, Acting based on the results, a germination greater than 70% was observed, as well as an increase of up to 30% in the size of the plant, with respect to production, was affected by atypical weather situations.
Conclusions: When using three different sowing dates, a normal trend was observed in the planting, germination and vegetative development process of the plant. With regard to grain production, the development of the crop was affected by the occurrence of atypical climatic phenomena, however the use of the PDCA methodology allows a better control of variables implicit in the establishment and development of the crop. continue with the research work to replicate the project by modifying sowing dates that will serve as a basis for future agricultural cycles which will allow us to anticipate these atypical situations, managing to control the process, without being affected by the climate of the region.