Acta Agronómica

Study area

The field research was carried out in the rural area of ​​the municipality of Anolaima-Cundinamarca, Colombia during the first half of 2015, in six farms under semi-ecological and conventional systems with an approximate extension of 1 ha, exclusively dedicated to plantain cv. Dominic Harton.

The area is located at the western flank of the eastern mountain range, at 1650 m.a.s.l., in temperate climate with temperatures ranged from 18-22°C, average annual rainfall of 1232 mm and relative humidity of 75 to 85%. The soils of the study area belong to the soil unit MQBe (Dystric Eutrudepts and Humic Eutrudepdts), characterized by the presence of high contents of expandable clays. The slopes can vary between 25-50% and form a heavily broken hill relief within a mountain landscape (Córdoba Vargas & León Sicard, 2013). It should be noted that plantain production was recently received in the area due to a commercial agreement that facilitated the inputs acquisition for this crop. In fact, there are basically two types of production as follows: conventional, which is based on the use of synthetic inputs; semi-ecological, which is involved with the use of practices that favor the soil care. (Figure 1).

Figure 1: Geographical location of the study area. Adapted from: IGAC (2000).

Experimental design

In order to minimize the experimental error and obtain the grouping of homogeneous experimental units, a randomized complete block design, was designed. In this sense, each of the farms, which belongs to the commercial agreement will be analyzed as a block, since each one individually has homogeneous conditions in its interior. However, factors such as the treatment (semi-ecological or conventional) confers them unique characteristics which modify the soil properties and the block analysis, favors the obtaining of conclusive data per treatment.

The choice of farms group, which is object of study was based on three criteria as follows: farms which had achieved commercial agreement and are established in the study area (60 farms); farms with same soil taxonomic unit, since each unit has unique soil characteristics and have allowed comparison among them; within the farms group with at least one of the treatments needs to be analyzed (conventional and semi-ecological), respectively. Under these criteria, 24 farms located in the "Chiniata" sector, belonging to the MQEb complex, were selected, ascribed to the agreement and with at least one of the treatments will be evaluated.

Therefore, farms were grouped according to the implemented treatment obtaining a group of 14 farms under a conventional system and another group of 10 farms under semi-ecological system. Finally, three farms from each of these groups were randomly selected to obtain a total of 6 farms, which were constituted as the study area of this research. Additionally, this last reduction was established based on the evaluation guide of soil quality and soil health taking a minimum 3 measurements per each soil management (Rosales, Pocasangre, Trejos, Serrano, Acuña, Segura, Delgado, Pattison, Rodriguez & Staver, 2015). The type of treatment and description practices in each evaluated farms are listed in Table1.

Table 1: Farm characteristics under evaluation

* Farm 1: Los Ocobos; Farm 2: El Laurel; Farm 3: Nuestros Sueños; Farm 4: El Paraíso; Farm 5: La Cajita; Farm 6: La Alcancía.

The soil sampling method in the judgment of an expert was used for the selection of sampling sites, in which, throughout the producer experience, the most representative area of ​​the farm was chosen, which in all cases was the one performed growth heterogeneity.

Therefore, two plots of 20 x 50 meters each (1000 m) were delimited and 20 plantain plants with a cluster between 13 and 16 weeks were selected with the aim to measure the biometric productivity parameters as follows: circumference diameter of the mother plant and number of hands, respectively.

Subsequently, were identified 1 plant per plot based on foliage, number of hands and cluster height. In front of each plot, a mini-calicata of 60*60*30 cm was established where soil samples were extracted for soil physical analysis. In fact, soil samples for chemical analysis were taken on the same experimental unit and throughout zig-zag path method seeking the greater data representativeness.

In this sense, ten soil subsamples were taken at soil depth of approximately 30 cm comparable in variability and based on the results obtained by Vargas, Serrano & Vargas (2011), where is concluded that most of the plant root system is in the first 30cm of soil depth, given the type of irrigation practiced on the evaluated farms. In fact, soil samples were then homogenized in a vessel and 1 kg of soil samples were extracted from the resulting mixture.

It was observed that all soil samples taken at field conditions, were packed in airtight, labeled and sealed bags, which were subsequently refrigerated in cold-storage boxes with a temperature of approximately 4°C. Finally, a portion of the samples was sent to the IGAC (Instituto Geogràfico Agustìn Codazzi) soil laboratory, where soil physical (texture) and chemical indicators (K, N, CO, pH), were evaluated.

The remaining portion was used to calculate soil bulk density and soil water depth (Castaño, Aristizábal, & González, 2011) (Table 2).

Data analysis

The statistical analysis was carried out through SPSS Software (r). For the selection of the most significant indicators, linear regressions were applied to soil physical, chemical and biological indicators with respect to the biometric indicators. Subsequently, correlation among variables and indicators were determined with the productivity indicator. Therefore, the selected indicators have allowed an explanation of the biometric variables behaviour related to soil quality (CMD). For instance, it was determined the independence among indicators, which carried out CMD throughout correlation analysis.

Table 2: Evaluated soil indicators

In addition, an analysis was applied for principal components in order to determine a relative importance of each indicator. Therefore, the indicators commonality was calculated to determine the weight of each agricultural soil and the corresponding response curve, obtaining the soil quality index for plantain cv. Dominic Harton using the soil quality index equation (Equation 1).

Equation 1

Where:

Weight i: weight of indicator; fi: response curve corresponding to indicator i

xi: value of indicator i of the place to be evaluated.

Conversely, for each indicators value x1, x2, ..., xn of the MDS, there is a value between 0 and 1 of the soil index, from which the closer to 1, can be translated into a better soil quality and how much closer to 0 or worse, indicates soil production (Villarreal et al. 2013). Finally, soil indicators were subjected to Mann-Whitney U analysis to determine whether or not, there was a significant difference among treatments.

Physical results

The soil apparent density does not present significant differences. However, T1 records are lowest than soils with greater porosity and infiltration. Conversely, all treatments, had achieved higher values ​​than the one desired for plantain cultivation.

Chemical results

pH and K in farms with T2, performed averaged values of 4.58 and 0.49, respectively. In fact, T1 reached on average a pH of 5.03, ratifying what was mentioned by Bulluck et al. (2002), who found soils with ecological practices where have allowed a decreasing in pH compared to evaluated areas under the influence of chemical fertilizers. Alternatively, potassium (K) reached a value of 0.30, which exposes a more balanced soil according to the general considerations from soil interpretation of the IGAC-soil national laboratory, where the average value oscillates between 0.2 and 0.4, respectively. In fact, SOC, and N, reached the highest registers in T1.

Biological results

The highest percentage of SOM- soil organic matter, indicates the presence of microbial activity and an increasing in root development in T1 (Askari & Holden, 2015). Therefore, microbial respiration had achieved a significant difference (p = 0.0418).

The biodiversity conservation indicator, have allowed significant differences among treatments (p = 0.001), based mainly on a greater richness in terms of macrofauna species which favors the soil organic matter biodegradation and a greater diversity of natural species that protect the structure and agroecosystem functioning (Amezaga, Elustondo, Crespo, Hortala, & Sierra, 2016; Garbisu, 2014).

Minimum set of data

Throughout linear regressions, soil indicators with highest significance for pseudostem were identified as follows: sand percentage, soil water depth, percentage of soil organic carbon (SOC), total nitrogen, K, biodiversity conservation and microbial respiration, respectively. For instance, the soil indicators for number of hands variables were as follows: percentage of sand, percentage of clay, apparent density, soil water sheet, pH, K and biodiversity conservation, respectively. The importance of soil indicators was determined according to parameters listed in Table 4.

Table 4: Regression coefficients for soil indicators

In Table 5, is shown the matrix correlation where is highlighted several relationships, which refers to the relationship between soil porosity and its ability to absorb water. Like sand, soil organic carbon (S.O.C.) and total nitrogen with positive correlation among them.

Table 5: Coefficients matrix

In addition, the matrix have allowed to distinguish fundamental relationships for vital soil components formation such as soil organic matter (positive correlation between soil organic carbon and total nitrogen). It also highlights the high correlation between biological activity and soil organic matter, which indicates a greater ability to decompose and helps to the nutrient absorption. In fact, sand content presents a high correlation with total nitrogen due to an increasing mineralization.

The main components analysis (PCA), have allowed to determine soil indicators set, which had achieved the greatest influence on the productivity variables behavior, as well as the first three components (pH, CO%, total N, K, biodiversity conservation and microbial respiration, respectively) may account for 86.4% of total variability.

Table 6, summarizes the results and defines the soil indicators that best explain the biometric characteristics throughout the counting of occurrences in each of the statistical techniques used (linear regressions for the circumference of the mother plant, linear regressions for the number of hands and ACP), choosing those that appeared in 2 of the 3 evaluated techniques.

Finally, the minimum data set (CMD) consisted of two soil physical indicators (% sand, water sheet), four chemical indicators (pH, percentage of soil Organic Carbon, Total Nitrogen, K, respectively) and two biological indicators (Biodiversity Conservation and microbial respiration).

Table 6: Set of minimum selection data

It was observed that a decreasing in values were obtained when is demonstrated a considerably high independence. The positive correlation between microbial respiration- soil organic carbon and microbial respiration-total nitrogen is highlighted, as it reflects a close relationship among constituent elements of soil organic matter and microbial activity, as well as the relevance of the biological indicators for a proper development of the agricultural soils (Ponge et al., 2013). It is worth noting the negative correlation between potassium-soil organic carbon and total potassium-nitrogen content.

Soil quality index

In the PCA of the selected indicators, three main components with a variance greater than 1 were obtained, which accumulated 89.224% of cumulative inertia (Table 7).

Table 7: Proper core values of component analysis

In the calculation of commonality for each CMD indicators, the relative importance of each soil indicator is demonstrated with respect to the three major components, these communalities helped to define the weights of each soil indicator within the construction of soil quality index (Table 8).

Table 8: Weight obtaining for each indicator

As indicated in Table 8, soil indicators with the greatest weight were as follows: pH, K, microbial respiration and sheet of water, respectively. In addition, it was found that the soil physical factor affects 23.94%, the soil chemical in 51.41% and the soil biological in 24.6%.

Although the supremacy of the influence percentage for soil chemical factor is obvious, the idea that a single factor provides a complete information since in the soil multiple variables obey to more than one factor interaction. (Table 9).

Table 9: Results of soil quality index

At first sight, the soil index for each treatment, shows an acceptable soil quality index and is comparable in variability to the report by Rosales et al. (2015). However, when submitting data to the Mann-Whitney U analysis, a significant difference can be observed among farms according to the type of treatment (bilateral asymptotic significance p=<0.05), although shows a better soil quality for T1, both per plot and farm, respectively.