Applying DRIS methodology in lettuce using regression analysis to split the sample population

Abstract

Lettuce (Lactuca sativa) is one of the most consumed vegetables in Colombia and department of Cundinamarca ranks first in terms of production. One of the problems that has been detected in the Bogotá savanna is that, although producers usually apply fertilizers in excess, the average yield obtained is well below the potential, largely because there is no tool to determine the appropriate nutritional status. This research proposed to determine the norms of the Diagnosis and Recommendation Integrated System (DRIS) for lettuce cultivation in the savanna of Bogotá, to balance the plant nutritional status previously selecting, using Monte Carlo analysis techniques, one of eight different methods to divide the population sample into high and low yield. Field data were collected from farms representing 12% of the area planted in 2018 according to Cundinamarca statistics. Plant nutritional status was determined at early vegetative growth using established laboratory techniques, and crop yield was recorded at the end. The splitting methods showed that the maximum difference with the lowest variance was obtained using the Cate and Nelson proposal applied on the predicted values of a regression analysis between yield and plant nutrient concentration, taking care that the size of the subpopulations was greater than or equal to 25% of the sample size. Once the DRIS standards were determined, DRIS indices were calculated for each sampling point and, as a result, potassium and phosphorus were the most frequent deficient elements due to imbalance, while iron and copper were the most frequent elements in excess.

Keywords:

• Critical level
• foliar diagnosis
• nutrient balance
• plant nutrition
• threshold value

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Government of Colombia, Ministry of Finance and Public Credit, which through AGROSAVIA financed this research under grant of $US20,000.