1. Introduction
The Hass avocado cultivar is established in the inter-Andean valleys, plateaus and foothills of the western and central Andes of Colombia at elevations ranging from 1600 to 2500 m a.s.l. [1,2]. These regions are characterized by high rainfall and low evapotranspiration with a consequent elevated leaching of cations from the soil and by soils with a predominance of loam to sandy loam textures with rapid drainage, strongly to moderately acidic pH and parent material with a predominance of allophanes with a high retention of anions such as phosphate, sulfate, molybdate and borate, as well as a low availability of these nutrients to crops [3,4].
Studies conducted on Hass avocados in Mexico have shown that the available mineral nutrients in the soil are related to the nutritional status of the trees [5]. In soils with acidic pH and low organic matter content, the concentrations of N, P and Ca were low in leaf tissue, while elements such as Fe and Zn were found in high concentrations [6,7]. Optimum nutrient values for avocado trees have been determined by observing which values led to higher tree production [7,8]. However, the nutritional content of trees can vary according to the specific environmental conditions of each production zone, which also influence phenological shifts in reproductive versus vegetative phenophases [9].
The Kenworthy balance index has been widely used to carry out nutritional diagnosis in Hass avocado trees [5,6,7,10]. This method considers the physiological variation in each nutrient in trees that are more productive as a reference to determine the tree nutritional requirement, and it serves as an aid in orchard fertilization [11]. The optimum fertilizer doses can then be calculated based on the nutrient-supplying capacity of the soil, the amount of nutrient removal with a planned yield and the potential for supplying fertilizer, as well as different factors related to fertilizer use [7,12,13].
Fruit nutrient removal varies according to a crop’s potential yield, making fertilization balanced, which improves the reincorporation of minerals extracted by the harvest into the soil, an important tool to satisfy the crop’s nutritional requirements [14,15]. We hypothesize that optimal fertilization based on harvest nutrient removal data can optimize nutrient applications that leave substantial residual nutrients in the soil. Additionally, plant analysis can be used as a guide to decide whether the amount of fertilizer applied improves the nutrient balance by indicating whether it is kept under normal ranges according to the standard nutrient values. Therefore, the aim of this study was to determine: (1) the effect of Kenworthy balance index (KBI)-based fertilization on fruit yield and size; (2) the specific nutritional requirements for a planned yield under tropical conditions; and (3) the nutrient removal by fruits.
2. Materials and Methods
2.1. Field Conditions
Homogeneous study areas were predefined according to the terrain relief and soil color and texture. In these areas, two commercial orchards with a density of 204 trees/ha of fruit-bearing 12-year-old “Hass” avocado trees grafted on seed rootstock, possibly of the Antillean ecological group, were selected, one in the municipality of Morales (M) and one in the municipality of El Tambo (ET) in the Cauca region of Colombia. The orchards were representative of this region, which has soils with andic properties originating from volcanic ash and igneous rocks that are deep and well-drained, with moderately coarse to fine texture and strongly to moderately acidic pH. The taxonomic classification of the soils of the orchards in the M and ET municipalities is Typic Hapludands subgroup Humic Pachic Dystrudepts [14]. According to the Koppen–Geiger climatic classification [15,16], the Cauca region is characterized as having a tropical rainforest climate (Af), with average annual temperature of 18 °C and typical annual rainfall of 1800–2500 mm. In all orchards, trees with different crop loads were often present in the same year, which is a characteristic of the alternate bearing behavior of this species. During two consecutive years, the daily wind speed (WS), precipitation (PPT), solar radiation (SR), relative humidity (RH) and maximum, mean and minimum air temperatures (TEMP) were recorded with a weather station (Watch Dog 2900ET; Spectrum Technologies, Plainfield, IL, USA) (M: 2°48′19″ N, 76°37′05″ W; ET: 2°25′54″ N, 76°44′58″ W). The climatic variables recorded were used to determine the daily reference evapotranspiration (ET0) based on the Food and Agriculture Organization (FAO) Penman–Monteith method and to calculate the water balance [17].
2.2. Soil Analysis
One 1000-m2 plot was selected per experimental orchard before applying the fertilizer treatments. Four soil samples of the top 45 cm layer were collected in August 2020, which corresponded to the low-rain season. The soil samples were taken from areas with higher root development under the tree canopy. The collected soil was dried, ground and sent to the laboratory at AGROSAVIA (Corporación Colombiana de Investigación Agropecuaria) for chemical analysis. The methods used in the analysis were the Walkley and Black method for organic matter determination [18] and Bray II for phosphorus (P) [19] determined in a UV–VIS spectrophotometer [19] (Thermo Scientific Spectronic Genesys 10 S). The B content was determined with monobasic calcium phosphate–azomethine H. The calcium (Ca), magnesium (Mg), potassium (K) and sodium (Na) contents were determined through extraction with ammonium acetate at pH 7.0 and were analyzed by atomic absorption spectroscopy (AAS; Agilent Technologies FS 240). The micronutrients iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) were quantified using the modified Olsen method, following the NTC 5526:2007 standard, using atomic absorption spectroscopy [20].
2.3. Fertilization Treatments
Four individual experiments were conducted over a period of two years, from August 2020 to April 2021 (year 1) and from March to November 2021 (year 2). These experiments were carried out in the two orchards located in ET and Morales M. Each experiment evaluated two fertilizer treatments. The first treatment corresponded to the farmer’s standard management (FT), and the second to the fertilization management plan based on the KBI (BT) (Table 1). The experiments were distributed as follows: Year 1: ET Orchard: Treatments FT and BT; Year 1: M Orchard: Treatments FT and BT; Year 2: ET Orchard: Treatments FT and BT; and Year 2: M Orchard: Treatments FT and BT.
A randomized complete block design was used, forming the blocks according to the slope of the terrain, with three blocks and five trees per treatment within each block. Each tree was considered an experimental unit and was selected based on qualitative characteristics such as high flowering level, vigor, and good phytosanitary conditions. Soil chemical analysis was performed on samples taken from the top 45 cm layer, as described in Section 2.2, to determine the soil’s nutrient supplying capacity (Equation (1)) [21]:
(1)
where FD = fertilizer dose in kg/ha; Ry = removal of mineral nutrients by fruits with a planned yield, kg/ha; SC = nutrient supplying capacity of the soil; and EF = fertilizer efficiency (%).The reference values for mineral removal by fruits were obtained from the study of Salazar-García and Lazcano Ferrat [22] on Haas avocados in Mexico (Table 1). Additionally, the nutrient removal by fruits for each element was determined in the same experimental orchards used in this work from the harvest of the second crop year (November 2021). The reference values for fertilizer efficiency were taken from reports on fertilizer use efficiency in the tropics and plant nutrition for food security [23,24]. In this work, efficiencies of 50% for N, 10% for P, 70% for K, and 60% for Ca and Mg were used. Details on the fertilization plan of each treatment are described in Table 1. The soil and plant analysis returned equal fertilization treatments based on the KBI for the M and ET orchards. For the KBI treatment, soil-applied fertilizer was applied monthly and distributed evenly around the drip line of the tree. A common characteristic of avocado trees observed in the crop areas investigated in this work was the overlap of different fruit growth phases in the same tree/date from different growing fluxes. The frequency of soil-applied fertilizer application was determined according to the nutrient requirements of the different fruit growth phases. The farmer’s standard management corresponded to the application of soil-applied fertilizer every two months.
2.4. Plant Nutrient Status and Fruit Nutrient Removal
2.4.1. Plant Nutrient Status and Foliar Standard Nutrient Concentration
The first leaf analysis was carried out to diagnose the tree nutrient status in August 2020 before applying the fertilizer treatments, and one year later in June 2021 for the FT and BT treatments evaluated. A sample of 40 leaves in the vegetative growth stage was taken on the first collection date from 10 trees at each orchard at a height of 1.5 m. On the second collection date, the sample was composed of 60 leaves from 15 trees for each treatment and each orchard. On both collection dates, three subsamples were removed to be analyzed separately.
The second leaf analysis was carried out to determine the foliar standard nutrient concentration from trees under KBI management as a reference for the Cauca region. The leaf samples were collected in June of 2022 and analyzed separately for 40 individual trees with yields greater than 85 kg/tree (Table S1), taken as a reference for the harvest recorded in November 2021. The high-yield trees comprised 20 trees in the KBI treatment from both orchards established in this work and an additional 20 trees under KBI management plus foliar fertilization with B and N. Table 2 summarizes the sampling date, taking as a reference the BBCH scale for Hass avocado proposed by Alcaraz et al. [25] to reference the phenological stage at each collection date.
2.4.2. Fruit Nutrient Removal
The mineral removal by the fruits for each element was determined from the harvest of the second crop year (April 2021) on 10 trees under KBI-based management per orchard with yields greater than 85 kg/tree for a planned yield of 20 t/ha. Ten fruits per tree were collected when the dry matter (DM) reached values corresponding to commercial maturity determined by the grower according to the practices of packing houses. The total weight of the fruits was determined, and four subsamples were obtained. Table 3 summarizes the sampling scheme adopted, taking as a reference the BBCH scale for Hass avocado proposed by Alcaraz et al. [25] to reference the phenological stage of the collection date.
2.4.3. Analysis Methods
The leaf samples corresponded to leaf blade plus petiole, fully expanded, mature but not senescent and from terminal shoots without fruit, oriented to the four cardinal points. The fruit samples corresponded to the flesh portion of the fruit. The samples were bagged and kept in a portable cooler prior to preparation for analysis. The leaf samples were washed with distilled water, dried at 70 °C in a forced air oven for 48 h and then ground in a stainless steel mill until they passed through a 20 mesh [26]. The flesh was dried at 70 °C in a forced convection oven (Memmert UF 110) to constant weight [27]. Samples were then sent to the laboratory at AGROSAVIA for mineral nutrient analysis. The following procedures were used: samples were digested with a diacid mixture (HNO3:H2O4 = 5:3 mL), and the quantifications of P, K, Ca, Mg, Fe, Mn, Zn, Cu and B were carried out by inductively coupled plasma emission spectroscopy. The Kjeldahl method with digestion in H2SO4 was used to determine N [28]. The total organic nitrogen was converted into (NH4)2SO4 and then distilled into a solution of B(OH)3 [29].
2.5. Kenworthy Balance Index
The diagnosis of the treatment established in the M and ET experimental orchards was carried out with the balance index method [7,11,30] to determine the plant nutrient status from the samples described in Section 2.4.1. This method allowed us to consider the physiological variations between trees with high yields, taking the foliar standard interval of each mineral element as a reference for the data obtained by Salazar-García and Lazcano-Ferrat [6]. The following equations were used to determine the balance index: (1) If the nutrient concentration of the sample (X) was lower than that of the standard interval (S), the influence of the variability (I) was added to the percentage value to obtain the balanced index. Therefore, if X < S (Equation (2)):
KBI = P + I.(2)
(2) If the value of the nutrient concentration in the sample (X) was higher than that of the standard (S), the influence of the variability (I) was subtracted from the percentage value to obtain the value of the balance index. Therefore, if X > S (Equation (3)):
KBI = P − I,(3)
in which KBI = Kenworthy balanced index; P (percentage of the standard) = (S/X) × 100; I (influence of the variation) = (100 − P) × (CV/100) for Equation (2); I (influence of the variation) = (P − 100) × (CV/100) for Equation (3); and CV = coefficient of variation of the nutrient in the standard population.The classification ranges suggested to interpret the balance indices for the nutrients in the sample are as follows [11]:
(a). Deficiency range: 17 to 50%;
(b). Below normal range: 50 to 83%;
(c). Normal range: 83 to 117%;
(d). Above normal range: 117 to 150%;
(e). Excess range: 150 to 183%.
Plant nutrient analysis based on the KBI method was incorporated to adjust the fertilization plan following the guidelines for the management of plant nutrients and their sources and the assessment of the available nutrient status of soils and plants proposed by Roy et al. (2006) [23], taking into consideration the following criteria:
The current plant nutrient status is determined before applying fertilizer treatments to allow us to conclude whether the soil nutrient supply keeps the plant at the normal range concentration at the foliar level;
Macronutrients within the normal, below normal and deficient ranges were applied in equal quantities to those removed by the harvested fruits when these values were lower than the nutrient-supplying capacity of the soil to minimize the depletion of soil nutrient reserves;
Minerals within the above normal and excess ranges were not included in the fertilization plan.
The details of plant fertilization adjusted for the KBI-based management treatment are farmer’s standard management treatment are described in Table 2. The information includes the source, chemical formula and amount of fertilizer applied for each treatment.
2.6. Fruit Size and Yield Variables
The production in kg/tree was recorded, and 100 fruits were randomly selected per treatment in the ET and M orchards. The central diameter of each fruit was measured in mm using a digital caliper (MTC500-196, Mitutoyo Corp., Sakado, Japan). Additionally, the individual weight of each fruit was obtained using a digital scale (SJ-6200E; Vibra; readability 0.1 g; Japan). The individual fruit weight data were used to classify the fruits according to the Codex Alimentarius standards based on their size (g/fruit). The classification establishes 10 sizes: 10 (364–462 g), 12–16 (227–274 g), 18 (203–243 g), 20 (184–217 g), 22 (165–196 g), 24 (151–175 g), 26 (144–157 g), 28 (134–147 g), 30 (123–137 g), and 32 (80–123 g) [31].
2.7. Statistical Analysis
A randomized complete block design consisting of two treatments (BT and FT) and three blocks distributed along the slope of the terrain was used. Each treatment consisted of five trees, with each tree considered an experimental unit. Years 1 and 2, as well as the ET and M orchards, were treated as independent experiments. Soil chemical variables and climatic variables were analyzed using descriptive statistics. For soil chemistry, means and standard errors (SE) were reported for four samples per orchard before the treatments were applied. Climatic variables were reported as monthly averages per orchard with the standard error of the daily value. The nutritional status of plant foliar tissue was diagnosed using classification values to interpret the nutrient balance indices through the KBI, as presented in Section 2.5. To analyze differences between fertilizer treatments in fruit diameter, fresh weight, and yield, a one-way analysis of variance (ANOVA) was performed. The response variables considered were fruit diameter (mm), fruit weight (g), and yield (kg/tree), with treatments as fixed factors. The means of each treatment were compared using Tukey’s HSD test with a significance level of p < 0.05. The distribution of fresh fruit weight for the two treatments was plotted according to the ranges defined by the Codex Alimentarius standard for avocados (Codex Stand 197, 1995) [31]. The nutritional standard was determined using leaf tissue samples taken from the selected trees, as described in Section 2.4.1, and the mean and coefficient of variation (CV) were calculated for all samples. To determine the nutrient removal by fruits from the samples described in Section 2.4.2, the mean and standard error of each nutrient were calculated. All statistical analyses were performed using R-Project for Statistical Computing version 4.2 within the RStudio environment version 2022.07.1.
3. Results
3.1. Soil Characteristics and Climatic Conditions
The chemical soil characteristics in the two orchards in M and ET, Cauca region, showed a sandy loam texture, with low cation exchange capacity (CEC) values (<10 cmol(+)/kg), a moderately acidic pH (5.38 ± 0.11 at M and 5.50 ± 0.07 at ET) and very poor cation availability. The low electrical conductivity (EC) values (<1 dS/m) indicate that they are salt-free soils. The macronutrients P, Ca and Mg, as well as the micronutrients Zn and B, showed low concentrations, while the elements K, Fe, Cu and Mn were found at normal levels. The organic matter (OM) contents were high (8.45 ± 0.24% at M and 6.76 ± 1.34% at ET), which is a possible indicator of N availability (Table 4).
The climatic conditions in both orchards and both crop years showed similar patterns. Both the temperature and relative humidity were constant throughout the year, with average maximum and minimum temperatures of 16 ± 0.1 °C and 27 ± 0.2 °C, respectively, and RH of 84 ± 1.4% (Figure S1). The water balance also followed a similar trend in the orchards at both M and ET and in both crop years. A low-rain season was recorded from July until September, with a negative water balance for the first crop year, and high-rain season periods lasting from October to December and January to June (Figure 1a,b) were also recorded. Differences between the orchards in average cumulative annual precipitation were recorded, with 2118 mm for M and 1842 for ET.
3.2. Fertilizer Treatments and Plant Nutrient Status
The nutritional diagnosis of trees before the fertilizer treatments showed an imbalance in some nutrients at the foliar level. The elements P, S, Fe, Mn and Zn were below the normal range, and Ca was in the deficiency range. The nutrients N, K, Mg and Cu were in the normal range, and B was in the above normal range (Figure 2). The leaf nutrient concentration results according to the KBI followed the following orders from highest to lowest: Mg > N > K > P > Ca for macronutrients and B > Cu > Fe > Mn > Zn for micronutrients.
The standard fertilizer plan applied by the farmer was based on fixed doses every two months for macro- and micronutrients. The N sources used by the farmer were both ammonium and nitrate in compound fertilizers with a total supplied of 217 kg/ha for the ET orchard and 117 kg/ha for the M orchard. The amounts supplied for the remaining macronutrients were 325 kg/ha P2O5, 101 kg/ha K2O, 218 kg/ha CaO and 30 kg/ha MgO for the ET orchard and 178 kg/ha P2O5, 104 kg/ha K2O, 36 kg/ha CaO and 18 kg/ha MgO for the M orchard. According to the nutrient analysis before the treatments were applied, B was the only nutrient in the above normal range. The fertilizer plan based on the KBI included application of 98.4 kg/ha N, 54.7 kg/ha P2O5, 117 kg/ha K2O, 3.8 kg/ha CaO and 15 kg/ha MgO. For micronutrients, only Mn was applied at the beginning of the treatments when there was a low content of it in the soil at the ET orchard (values calculated from Table 1).
For the analysis carried out in June 2021, both macro- and micronutrients showed a similar trend to the analysis carried out before the treatments were applied following the farmer’s standard management plan (Figure 3A). The plant nutrient concentrations of both macro- and micronutrients could be maintained in the normal range based on the KBI and considering the reincorporation into the soil of minerals extracted by the harvested fruits (Figure 3B,D).
3.3. Fertilizer Treatments on Yield Variables
The KBI-based fertilization treatment showed different effects. In the first crop year, no significant differences were found in fruit diameter or fresh weight between the ET and M orchards (p = 0.1613 and p = 0.4067, respectively) (Table 5). However, in the second crop year, with flowering starting in March, significant differences were observed between the treatments for the fruit diameter variable (p = 0.0258; p = 0.0237) in both the ET and M orchards. The KBI-based treatment resulted in fruits with a larger diameter. Additionally, an increase in fresh fruit weight was observed in the M orchard (p = 0.0292), while in the ET orchard, the increase in fresh fruit weight was not statistically significant (Table 5).
No significant differences were found in the yield per tree between the treatments evaluated in the first crop year (p = 0.05 for the family of comparisons) in either the ET or M orchards. However, in the second crop year, a significantly higher yield was observed with the fertilization management plan based on the KBI (BT) compared to the farmer’s standard management plan (FT) in both the M and ET orchards (Table 6).
In the ET orchard during the first year, no significant differences were found between the treatments, with average yields per tree of 56.4 ± 6.6 and 58.1 ± 9.1 kg/tree for the FT and BT treatments, respectively (Table 6). The same trend was observed in the M orchard, with average yields of 38.4 ± 5.4 kg/tree and 30.0 ± 5.0 kg/tree for the FT and BT treatments, respectively.
In the second crop year, a higher yield was recorded in the KBI treatment in both orchards. In the ET orchard, the KBI-based treatment showed a significantly (p = 0.037) higher yield (63.3 ± 6.55 kg/tree) than the FT treatment (45.9 ± 5.5 kg/tree) according to the post hoc Tukey’s HS) test. A similar finding was observed in the M orchard, where the BT treatment yielded a significantly higher average yield of 73.5 ± 6.35 kg/tree compared to the FT treatment, which had an average yield of 55.3 ± 6.35 kg/tree (p = 0.046).
It is worth noting that the crop years and the M and ET orchards were treated as individual experiments, thus avoiding direct comparisons between the first and second crop years and between the orchards.
Nutritional management based on the KBI also increased fruit size and fresh weight. The fruit size classification was based on the Codex Alimentarius standard (Codex Alimentarius Standard 197, 1995) [32]. In the M orchard, during the first crop year, no significant differences were found in fruit size between the treatments. Under the farmer’s standard management, fruit sizes were classified between 32 (80–123 g) and 28 (134–147 g), while with KBI-based management, sizes larger than 24 (151–175 g) were achieved (Figure 4A). In the second crop year for the M orchard, the same trend observed in the first year was maintained. A similar distribution pattern in fruit size was observed, but with a clear improvement in larger sizes, especially those greater than 22 (165–196 g) (Figure 4B).
In the ET orchard, during the first crop year, no significant effect of the KBI-based fertilization management was observed on fruit size (Figure 4C). However, in the second crop year, there was variation in the fruit size between treatments. An increase in the percentage of fruit size 24 (151–175 g) or larger was achieved with nutritional management based on the KBI (Figure 4D).
These results indicate that the fertilization management plan based on the KBI positively influenced fruit size and fresh weight in the M orchard, leading to a higher yield in terms of harvested fruit size. Although no significant differences were found in the ET orchard during the first year, improvements in fruit size were observed in the second year with KBI-based management.
3.4. Foliar Standard Nutrient Concentration
The foliar standard nutrient concentration calculated from trees with yields higher than 85 kg is shown in Table 7, as well as the optimal values reported for some avocado production areas around the world. The macronutrient concentrations at the foliar level showed that N, Ca and K had the highest levels, with 2.37%, 1.36% and 1.09%, respectively. The Mg and P nutrients showed lower levels of 0.32% and 0.15%, respectively. The micronutrients with the highest concentrations were Mn (116.77 mg kg−1), Fe (70.35 mg kg−1) and Zn (30.04 mg kg−1), and those with the lowest concentrations were B and Cu (Table 7). The following sequence, from highest to lowest values, was obtained for the foliar concentration levels estimated for the Cauca region: N > Ca > K > Mg > P > Mn > Fe > Zn > B > Cu (Table 7). The coefficients of variation (CVs) showed variability for the minerals evaluated under field conditions, with values ranging from 13.11% to 51.08%.
3.5. Fruit Nutrient Removal
The amount of nutrients extracted by the fruits for a planned yield of 20 ton/ha is shown in Table 8. K and N were the macronutrients with the highest fruit extraction values (82.4 ± 1.62 and 51.9 ± 1.33, respectively). Although P is a primary nutrient, it showed values close to those of secondary nutrients such as Mg, Ca and S (Table 8). The following sequence, from highest to lowest fruit nutrient extraction values, was obtained: K > N > P > S > Mg > Ca > B > Fe > Zn > Cu > Mn.
4. Discussion
The soils in the experimental areas located in the Cauca region showed a low CEC, leading to a low cation availability, which is also affected by the acidic soil conditions. Additionally, high cumulative annual precipitation is common in this region and is higher than evapotranspiration, which causes leaching from the soil profile of the most soluble ions, such as Ca+2, Mg+2, Na+ and K+. At the same time, the metallic cations Al+3, Fe+3 and Mn+4 are released by hydrolysis, leading to acidity of the soil [34,35]. The Cauca region has soils with Andic characteristics, and allophane (amorphous material commonly associated with the clay-mineral halloysite) and imogolite are the most predominant clays, with high specific surface area and abundant reactive sites for phosphorus retention. [36,37,38,39,40,41]. These conditions require external nutrient applications to augment soil supplies that are removed with harvest as well as to minimize the depletion of soil nutrient reserves.
A common practice among farmers in the experimental areas is to apply high amounts of fertilizer for a crop year. Additionally, farmers use compound fertilizer with high ammonium concentrations to supply macronutrient requirements. The nitrification of ammonium fertilizer, used as a source of N, increases the acidic soil conditions because of the release of H+ [42,43,44]. This condition may also affect the interaction between mineral nutrients and their availability to plants. Soil analysis showed low concentrations of P and Ca, and in the plant analysis, these nutrients were also found in the below normal range for P and deficient range for Ca despite the higher doses applied by farmers. A direct relationship has been reported between the nutritional status of avocado trees and the mineral availability in soils. Some characteristics, such as acidic pH, low levels of OM and high levels of Cu, Fe, K, Ca, B and Zn, lead to a foliar nutritional imbalance [5,7,45]. The Andic soil is a group with a high specific surface area in clay minerals and reactive sites for phosphate retention by non-crystalline aluminum and iron materials [46,47]. The phosphorus applied to the soil reacts rapidly with these materials and ends up forming insoluble metal–phosphorus components in which active aluminum and iron play a preponderant role [47,48]. The acidic soil characteristics in this study led to low CEC and low availability of base cations (Ca2+, Mg2+, Na+ and K+). In different soils, the Ca fixed in the colloidal complex and that bound to humic compounds are the most abundant natural forms. Even in acidic soils, there are almost always enough quantities of Ca for adequate plant nutrition, especially when species are not highly demanding [49]. As mentioned in other studies, an excess of Ca, Mg or K fertilizer may induce deficiencies due to antagonism between these elements [50,51].
In the fertilization management plan based on the KBI, calcium nitrate, diammonium phosphate (DAP) and urea were used as N sources. It is well known that urea has lower acidity than ammonium sulfate (AS). Each mole of AS produces four moles of H+, whereas each mole of urea and ammonium nitrate produces only two moles of H+ [44,52]. The absorption of N by plants in the form of NO3− occurs mainly through mass flow due to its anionic form which does not adhere to clay minerals and remains in the soil solution [53,54]. The N fertilizer doses applied herein match those in previous reports for Hass avocados in California with requirements of 101 kg/ha [55]. Avocado fruits have a large protein content and are a sink for C and N. The average amount of N in avocado fruits in California is 5.0 to 7.5 g per fruit, representing more than 1 g of N per fresh fruit as an indicator of the N requirement [56].
As a rule, the soil pH condition affects the availability of B, Cu, Fe, Mn and Zn, with their availability increasing when the soil pH decreases [57,58]. In this study, the application of micronutrients was not considered given the normal content in the soil, and only Mn was applied when the content in the soil and plant tissue was low. It was also confirmed that higher doses of P fertilizer applied by the farmer with have an effect on Fe availability. Previous studies have reported low availability of P in most tropical and subtropical soils because of strong phosphate adsorption on soil minerals [59,60,61,62]. Phosphorus adsorption and desorption depend on the crystallinity of clay minerals, specific surface area (SSA) and the configuration and concentration of hydroxyl groups on the surface of iron [63]. Some studies reported on the preferential phosphorus adsorption by hydroxyl surface groups in iron oxides, which are protonated below pH 7–9 [39,62].
The soils in this study with Andic characteristics have allophane with one of the most predominant clay minerals with large specific surface areas (700–1500 m2/g) and react strongly with anions, such as phosphate [41]. OM has no direct role in soil acidification and can act in the process of cation exchange [58,64]. An effect of the OM on P availability has been reported [62]. These studies suggest that OM can increase negative surface charge, have a low binding energy for adsorbed P and can therefore increase the efficiency of phosphate fertilizer [65,66,67]. In this study, the soils showed a high content of OM, and although the decomposition rate depends on temperature, humidity and other soil conditions that influence microbial activity [68,69,70], the high content of OM should be considered as a source to supply additional crop requirements.
At the foliar level, Ca and P were found in the normal range one year after the fertilizer treatment. The amount of fertilizer applied based on KBI management in this study was in accordance with the amount removed by the harvest reported by other studies [7,22]. Research studies conducted in Mexico have reported nutrient balance for fertilization management plans adjusted with the KBI method, and the best results for yield per tree were obtained with high doses of P2O5 (115.752 kg) and K2O (393.12 kg) and an increase in the yield of 20 kg/tree [71]. In our study, we found that the fertilization plan based on KBI management significantly improved the average yield per tree after application for two crop years and achieved better performance in terms of fruit size and fresh weight without increasing the fertilizer doses.
Foliar analyses are an important tool for the nutritional diagnosis of perennial crops such as avocado. Therefore, nutritional chemical analyses are a valuable complement in estimating nutrition in plants [72]. Optimal nutrient concentrations in Hass avocado trees have been estimated in different environments and regions of the world. Although optimal or standard values show little variation in the leaves, reported values are within the following ranges: 2.0–2.5% N; 0.1–0.25% P; 1–2.0% K; 1.5–2% Ca; 0.5–0.7% Mg; >50 ppm Fe; >80 ppm Mn; >30 ppm Zn; >7 ppm Cu; and >20 ppm B [7,72]. The values reported in the experimental areas of the Cauca region are within the ranges reported worldwide.
An imbalance of foliar nutrients was found herein with the farmer’s nutritional standard management, with low contents of Ca, P, S, Fe and Zn. A direct relationship has been reported between the nutritional status of avocado trees and mineral availability in soils. Some characteristics, such as acidic pH, low levels of OM and high levels of Cu, Fe, K, Ca, B and Zn, lead to foliar nutritional imbalance [5,7,45]. The KBI method is suitable for implementing fertilization plans in the study area of the Cauca region because it considers the standard value for each nutrient from trees with a high yield and the standard deviation of the content for each nutrient in this tree population. KBI has been widely used to diagnose the nutrimental status of Hass and Fuerte avocados in Mexico [6,7,10,73,74].
The ongoing nutrient requirements during fruit growth were supplied monthly with fertilizer management based on the KBI. In Mexico, it was found that fertilization treatments based on tissue analysis improved the nutritional status of Hass avocado trees after fertilization. This was reflected in better soil fertility and increased production [5]. Other studies have also reported on the benefits of applying the KBI method, specified by site in Hass avocado production, which has led to an average increase in crop yield approaching the potential of 32.5 t/ha. In addition, previous studies coincide with the results of our study in that fertilization under KBI increased fruit size; after two years, fruit weight and size increased, with a high proportion of fruits weighing more than 195 g [12].
5. Conclusions
The Cauca region has soils with Andic characteristics, such as acidic pH and low CEC, that affect the cation availability necessary for plant growth and development. This condition and the large amount of fertilizer applied by farmers led to an imbalance of nutrients for Hass avocado trees that affected parameters such as yield per tree and fruit growth recorded at harvest time. From the diagnosis of tree nutritional status based on the KBI method, and considering the nutrient content extracted by fruits as well as the nutrient availability in the soil, both yield and fruit growth were improved. As a common practice for the farmers in the experimental areas of this work, high amounts of fertilizer are used as well as compound fertilizer with high ammonium concentrations to supply macronutrient requirements, such as N. The KBI can be used as a tool to determine the nutritional status of plants under a specific fertilizer management plan and to evaluate if the amount of fertilizer applied keeps each nutrient in normal ranges according to standard values. In this way, the results obtained herein indicate that applying balanced amounts of the most limiting nutrients achieves better performance in increasing the yield when fertilization is fine-tuned to local soil chemical conditions and crop requirements while minimizing nutrient losses.
A.R.-R.: conceptualization, methodology, data curation, writing—original draft, formal analysis, supervision, project administration, funding acquisition. R.A.B.-D.: methodology, formal analysis, writing—original draft. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
The authors express their gratitude to the orchard growers, the support team and Palmira Research Centers (Agrosavia) for their support in the technical execution of this study and to the Department of Cauca (Colombia) for funding.
The authors declare no conflict of interest. The funders had no role in the design of the study or in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Climatic variables for Hass avocado experimental orchards in the Cauca region: (A) water balance for the M orchard; (B) water balance for the ET orchard. ET0, reference evapotranspiration (FAO Penman–Monteith method); PPT, monthly accumulated precipitation.
Enlarge this image.Figure 2. Nutrient status of Hass avocado trees before the fertilizer treatments at the M and ET orchards. The bars represent the standard error (SE).
Enlarge this image.Figure 3. Kenworthy balance index for macronutrients and micronutrients according to ranges in Persea americana var. Hass: (A) El Tambo FT; (B) El Tambo BT; (C) Morales FT; (D) Morales BT. The bars represent the standard error (SE).
Enlarge this image.Figure 4. Relative frequency of fruit size classes according to the Codex Alimentarius standard (Codex Stand 197, 1995) under the farmer’s standard nutritional management (FT) and KBI-based management (BT). (A,B) M orchard; (C,D) ET orchard. Values for 100 fruits per treatment.
Fertilizer treatments and sources.
| Fertilization Treatment | Fertilizer |
Fertilizer |
Amount of Fertilizer (kg/ha) Year | Fertilizer Chemical Form | % Nutrient |
|---|---|---|---|---|---|
| BT | Urea | CH4N2O | 90.4 | CH4N2O | 46 |
| Diammonium phosphate (DAP) | (NH4+)2HPO4 | 119 | P2O5 | 46 | |
| NH4+ | 18 | ||||
| Potassium nitrate | KNO3 | 255 | K2O | 46 | |
| NO3 | 13 | ||||
| Calcium nitrate | Ca(NO3)2 | 14.2 | CaO | 27 | |
| NO3 | 16 | ||||
| Magnesium sulfate | MgSO4 | 52.4 | MgO | 15 | |
| S | 12 | ||||
| Manganese chelate | C10H12N2O8Na2Mn | 0.34 | Mn | 13 | |
| El Tambo (ET) |
NPK 10-30-10 | Compound fertilizer N 10% (NH4+), P 30% (P2O5), K 10% (K2O) | 1000 | NH4+ | 8.2 |
| NO3 | 1.8 | ||||
| P2O5 | 30 | ||||
| K2O | 10 | ||||
| Nitrabor® (YARA; Olso, Norway) | Compound fertilizer N 15.4% (NH4+, NO3−), CaO 25.5%; B 0.3% | 500 | NH4+ | 1.3 | |
| NO3 | 14.1 | ||||
| CaO | 25.5 | ||||
| H3BO3 | 0.3 | ||||
| AGRIMINS® (Colinagro; Bogotá, Colombia) |
Compound fertilizer major and minor elements | 500 | NH4+ | 1.0 | |
| CH4N2O | 7.0 | ||||
| P2O5 | 5.0 | ||||
| CaO | 18 | ||||
| MgO | 6.0 | ||||
| S | 1.6 | ||||
| B | 1.0 | ||||
| Cu | 0.14 | ||||
| Mo | 0.005 | ||||
| Zn | 2.5 | ||||
| Morales (M) |
NPK 10-30-10 | Compound fertilizer N 10% (NH4+), P 30% (P2O5), K 10% (K2O) | 500 | NH4+ | 8.2 |
| NO3 | 1.8 | ||||
| P2O5 | 30 | ||||
| K2O | 11 | ||||
| Remital ® (YARA; Olso, Norway) | Compound fertilizer N 17% (NH4+. NO3−), P 6% (P2O5), K |
300 | NH4+ | 9.7 | |
| NO3 | 7.3 | ||||
| P2O5 | 6 | ||||
| K2O | 18 | ||||
| MgO | 2 | ||||
| S | 1.6 | ||||
| B | 0.2 | ||||
| Zn | 0.1 | ||||
| AGRIMINS® (Colinagro; Bogotá, Colombia) |
Compound fertilizer major and minor elements | 200 | NH4+ | 1.0 | |
| CH4N2O | 7.0 | ||||
| P2O5 | 5.0 | ||||
| CaO | 18.0 | ||||
| MgO | 6.0 | ||||
| S | 1.6 | ||||
| B | 1.0 | ||||
| Cu | 0.14 | ||||
| Mo | 0.005 | ||||
| Zn | 2.5 |
Sampling scheme used to determine the plant nutrient status and standard nutrient concentration.
| Treatment | Collection Date | Orchard | Number of Trees | Number of Samples | BBCH Phenological Scale | |
|---|---|---|---|---|---|---|
| Plant Nutrient Status | BT and FT | August 2020 | M and T | BT and FT: 10 | One samples of 40 leaves per orchard | 514—Compound inflorescence separated: individual inflorescences separated and beginning of inflorescence elongation; |
| June 2021 | M and ET | BT: 15 |
One sample of 60 leaves per treatment per orchard | 510—Reproductive buds dormant: buds covered with green–brown bud scales with no sign of growth; |
||
| Foliar Standard Nutrient Concentration | BT | June 2022 | M and ET | 40 | 32 leaves per tree | 510—Reproductive buds dormant; buds covered with green–brown bud scales with no sign of growth; |
FT, farmer’s standard nutrient management; BT, nutrient management based on the KBI (BT); M, Morales orchard; ET, El Tambo Orchard.
Sampling scheme used to determine nutrient removal by fruits.
| Treatment | Collection Date | Orchard | Number of Trees | Number of Samples | BBCH Phenological Scale | |
|---|---|---|---|---|---|---|
| Fruit nutrient removal | BT | April 2021 | M and ET | 10 | Four subsamples from 10 fruits per tree. | 719—90% or more of final fruit size: fruit ready for commercial harvest; |
BT, nutrient management based on the KBI (BT); M, Morales Orchard; ET, El Tambo Orchard.
Soil nutritional content in orchards located in Morales (M) and El Tambo (ET) and interpretation according to the range classification proposed by Baquero Peñuela [
| ET | SE | Interpretation | M | SE | Interpretation | |
|---|---|---|---|---|---|---|
| pH | 5.3 | 0.070 | Moderately | 5.64 | 0.114 | Moderately |
| acidic | acidic | |||||
| CEC (cmol+/kg) | 5.38 | 0.682 | Low | 4.64 | 1.13 | Low |
| EC (dS/m) | 0.43 | 0.054 | Low | 0.63 | 0.116 | Low |
| OM (%) | 8.22 | 1.34 | High | 8.45 | 0.237 | High |
| K (cmol+/kg) | 0.37 | 0.050 | Normal | 0.62 | 0.120 | High |
| P (mg/kg) | 6.89 | 0.888 | Low | 3.91 | 0.485 | Low |
| Mg (cmol+/kg) | 1.04 | 0.155 | Low | 0.97 | 0.242 | Low |
| Ca (cmol+/kg) | 3.73 | 0.554 | Normal | 2.87 | 0.798 | Low |
| S (mg/kg) | 23.14 | 20.1 | High | 27.47 | 13 | High |
| B (mg/kg) | 0.26 | 0.047 | Normal | 0.29 | 0.046 | Normal |
| Fe (mg/kg) | 87.46 | 14.3 | Normal | 99.24 | 15.3 | Normal |
| Mn (mg/kg) | 3.94 | 1.17 | Low | 14.08 | 3.21 | High |
| Cu (mg/kg) | 2.97 | 0.555 | Normal | 3.82 | 0.731 | High |
| Zn (mg/kg) | 3.76 | 1.64 | High | 1.44 | 0.472 | Normal |
Values represent the mean of four samples per orchard; SE = standard error.
Fruit diameter and fresh weight at harvest time for two consecutive crop years.
| Crop Year | Orchard | Treatment | Fruit |
CV | SE | F |
Pr > F | Fruit Fresh Weight (g) | CV | SE | F |
Pr > F |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | ET | FT | 57.31 | 3.50 | 0.319 | 2.26 | 0.1613 | 121.33 | 3.50 | 1.606 | 2.02 | 0.1613 |
| BT | 55.29 | 119.97 | ||||||||||
| M | FT | 58.90 | 2.49 | 0.364 | 1.14 | 0.4067 | 138.76 | 6.82 | 2.653 | 1.69 | 0.2322 | |
| BT | 50.45 | 151.14 | ||||||||||
| 2 | ET | FT | 59.26a | 1.53 | 0.325 | 4.19 | 0.0258 | 139.94a | 6.49 | 2.849 | 2.25 | 0.1290 |
| BT | 61.96b | 160.81a | ||||||||||
| M | FT | 60.58a | 1.36 | 0.280 | 4.31 | 0.0237 | 143.44a | 4.12 | 2.02 | 4.02 | 0.0292 | |
| BT | 63.33b | 163.28b |
Values represent the mean of 100 fruits. CV, coefficient of variation; SE, standard error. ANOVA, p = 0.05; post hoc Tukey’s HSD test, p = 0.05.
Yield (average production, kg/per tree) of Hass avocados in each orchard with the farmer’s standard management plan (FT) and fertilization management plan based on the KBI (BT) in the Morales (M) and El Tambo (ET) orchards.
| Crop Year | Orchard | Treatment | Yield kg/Tree | CV | SE | F |
Pr > F |
|---|---|---|---|---|---|---|---|
| 1 | ET | FT | 56.4a | 37.25 | 6.60 | 0.02 | 0.888 |
| BT | 58.1a | 49.39 | 9.01 | ||||
| M | FT | 38.4a | 50.00 | 5.46 | 0.04 | 0.833 | |
| BT | 30.0a | 43.26 | 5.09 | ||||
| 2 | ET | FT | 45.9a | 40.84 | 5.50 | 5.04 | 0.037 |
| BT | 63.3b | 35.11 | 6.55 | ||||
| M | FT | 55.3a | 41.18 | 6.35 | 4.44 | 0.046 | |
| BT | 73.5b | 30.73 | 6.35 |
CV, coefficient of variation; SE, standard error. ANOVA, p = 0.05; post hoc Tukey’s HSD test, p = 0.05.
Standard nutrient concentrations for the Kenworthy balance index (KBI) of Persea americana var. Hass.
| Nutrient | Salazar-García and Lazcano-Ferrat [ |
Maldonado et al. [ |
This Study |
|||
|---|---|---|---|---|---|---|
| Standard | CV | Standard | CV | Standard | CV | |
| Nitrogen (%) | 2.35 | 10.9 | 2.11 | 8.79 | 2.37 | 13.52 |
| Phosphorus (%) | 0.14 | 11.1 | 0.15 | 13.66 | 0.15 | 13.11 |
| Potassium (%) | 1.37 | 15.16 | 0.93 | 15.38 | 1.09 | 23.2 |
| Calcium (%) | 1.86 | 17.6 | 1.92 | 34.54 | 1.36 | 16.45 |
| Magnesium (%) | 0.58 | 15.7 | 0.68 | 11.56 | 0.32 | 20.22 |
| Sulfur (%) | 0.4 | * | * | * | 0.21 | 16.08 |
| Iron (mg·kg−1) | 91 | 38.9 | 90.20 | 15.08 | 70.35 | 26.05 |
| Manganese (mg·kg−1) | 240 | 38.9 | 134 | 35.73 | 116.77 | 51.08 |
| Zinc (mg·kg−1) | 27 | 32.8 | 34.90 | 43.98 | 30.04 | 29.73 |
| Copper (mg·kg−1) | 10 | * | 19.50 | 66.18 | 7.51 | 21.05 |
| Boron (mg·kg−1) | 75 | * | 238 | 47.51 | 18.69 | 25.1 |
* Not reported. CV, coefficient of variation. References of standard nutrient concentration [
Fruit nutrient removal in Hass avocado for a planned yield (kg per 20 t of fresh fruit).
| Nutrients | Fruit Nutrient Removal (kg/ha) | ||
|---|---|---|---|
| Salazar García and |
Maldonado et al. (2007) [ |
This Study (2023) | |
| N | 51.5 | 54.6 | 51.9 ± 1.33 |
| P2O5 | 20.6 | 14.4 | 7.1± 0.34 |
| SO4 | 6.9 | nr | 4.9 ± 0.12 |
| CaO | 1.7 | 4.6 | 2.8 ± 0.19 |
| MgO | 5.9 | 30 | 4.8 ± 0.24 |
| K2O | 93.8 | 80 | 82.4 ± 1.62 |
| Fe | 0.12 | 0.19 | 0.13 ± 0.01 |
| Cu | 0.04 | 0.05 | 0.04 ± 0.001 |
| Mn | 0.02 | 0.03 | 0.03 ± 0.002 |
| Zn | 0.08 | 0.08 | 0.1 ± 0.004 |
| B | 0.08 | 0.11 | 0.24 ± 0.03 |
SE, standard error; 20 trees at harvest time.
Supplementary Materials
The following supporting information can be downloaded at
References
1. Ramírez-Gil, J.G.; Morales, J.G.; Peterson, A.T. Potential Geography and Productivity of “Hass” Avocado Crops in Colombia Estimated by Ecological Niche Modeling. Sci. Hortic.; 2018; 237, pp. 287-295. [DOI: https://dx.doi.org/10.1016/j.scienta.2018.04.021]
2. Bernal-Estrada, J.; Vázquez-Gallo, L.; Valenzuela, C.; Salazar-García, S.; Barrientos-Priego, A.F. Fenología Del Aguacate Cv. Hass Plantado En Diversos Ambientes Del Departamento de Antioquia, Colombia. Proceedings of the Memorias del V Congreso Latinoamericano del Aguacate; Ciudad Guzmán, Mexico, 4–7 September 2017; pp. 4-7.
3. Gómez, G.L.; Caballero, R.A.; Baldion, R. Ecotopos Cafeteros de Colombia; Cenicafe: Bogotá, Colombia, 1991.
4. Bravo, I.; Montoya, J.C.; Menjivar, J.C. Retención y Disponibilidad de Fósforo Asociado a La Materia Orgánica En Un Typic Melanudands Del Departamento Del Cauca, Colombia. Acta Agron.; 2013; 62, pp. 261-267.
5. García-Martínez, R.; Cortés-Flores, J.I.; López-Jiménez, A.; Etchevers-Barra, J.D.; Carrillo-Salazar, J.A.; Saucedo-Veloz, C. Rendimiento, Calidad y Comportamiento Poscosecha de Frutos de Aguacate ‘Hass’ de Huertos Con Diferente Fertilización. Rev. Mex. Cienc. Agric.; 2021; 12, pp. 205-218. [DOI: https://dx.doi.org/10.29312/remexca.v12i2.2232]
6. Salazar-García, S.; Lazcano-Ferrat, I. Diagnóstico Nutrimental Del Aguacate ‘Hass’ Bajo Condiciones de Temporal. Rev. Chapingo Ser. Hortic.; 1999; 5, pp. 173-184.
7. Maldonado-Torres, R.; Álvarez-Sánchez, M.E.; Almaguer-Vargas, G.; Barrientos-Priego, A.F.; García-Mateos, R. Estándares nutrimentales para aguacatero “hass”. Rev. Chapingo Ser. Hortic.; 2007; 13, pp. 103-108. [DOI: https://dx.doi.org/10.5154/r.rchsh.2006.11.051]
8. Palacios Alvarado, J.M. Dinámica y Balance Nutrimental En Árboles de Aguacate (Persea americana Mill.) Cv. Hass, Con Alto y Bajo Rendimiento En La Región de Uruapan, Michoacán. Master’s Thesis; Colegio de Postgraduados: Chapingo, Mexico, 1986.
9. Salazar-García, S.; Medina-Carrillo, R.E.; Álvarez-Bravo, A. Influencia Del Riego y Radiación Solar Sobre El Contenido de Fitoquímicos En La Piel de Frutos de Aguacate ‘Hass’. Rev. Mex. Cienc. Agric.; 2017; 7, pp. 2565-2575. [DOI: https://dx.doi.org/10.29312/remexca.v0i13.483]
10. Hernández-Valdés, E.F.; Aguilar-Campoverde, S.; Aguiera-Taylor, V.; Pérez-Sánchez, R.E. Concentración Nutrimental En Hojas de Aguacate “Hass” Con Síntoma de Moteado. Rev. Mex. Cienc. Agric.; 2012; 3, pp. 621-627.
11. Kenworthy, A.L. Leaf Analysis as an Aid in Fertilizing Orchards. Soil Testing and Plant Analysis; Soil Science Society of Americ: Madison, WI, USA, 1973; pp. 381-392.
12. Salazar-García, S.; Lazcano-Ferrat, I. Site Specific Fertilization Increased Yield and Fruit Size in ‘Hass’ Avocado. Better Crop. Int.; 2003; 17, pp. 12-15.
13. Szűcs, E. Possibilities to Meet Nutritional Requirements of Fruit Free and Environmental Production. Acta Hort.; 1997; 448, pp. 433-437. [DOI: https://dx.doi.org/10.17660/ActaHortic.1997.448.80]
14. IGAC Instituto Geográfico Agustín Codazzi. Estudio General de Suelos y Zonificación de Tierras de Cauca; IGAC: Bogotá, Colombia, 2009.
15. Peel, M.C.; Finlayson, B.L.; McMahon, T.A. Updated World Map of the Köppen-Geiger Climate Classification. Hydrol. Earth Syst. Sci.; 2007; 11, pp. 1633-1644. [DOI: https://dx.doi.org/10.5194/hess-11-1633-2007]
16. Beck, H.E.; Zimmermann, N.E.; McVicar, T.R.; Vergopolan, N.; Berg, A.; Wood, E.F. Present and Future Köppen-Geiger Climate Classification Maps at 1-Km Resolution. Sci. Data; 2018; 5, 180214. [DOI: https://dx.doi.org/10.1038/sdata.2018.214] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30375988]
17. Allen, R.G.; Pereira, L.S.; Raes, D. Evapotranspiración del Cultivo Guías Para la Determinación de los Requerimientos de Agua de los Cultivos; FAO Food and Agriculture Organization: Rome, Italy, 2006.
18. GLOSOLAN.FAO. Standard Operating Procedures for Soil Organic Carbon: Walkley and Black Method. GLOSOLAN’s Best Practice Manual; Food and Agriculture Organization of the United Nations: Rome, Italy, 2020.
19. GLOSOLAN.FAO. Standard Operating Procedure for Soil Available Phosphorus Bray I and Bray II Method; Food and Agriculture Organization of the United Nations: Rome, Italy, 2021.
20.
21. Decasa, A.S.; Trejo, A.F.; Vargas, G.A.; Hernández, J.; Hernández, M.A. Study Soil Analysis in Alamo, Temapache and Veracruz, to Generate Doses of Fertilization in Orange. Agroind. Sci.; 2018; 8, pp. 89-94. [DOI: https://dx.doi.org/10.17268/agroind.sci.2018.02.02]
22. Salazar-García, S.; Lazcano-Ferrat, I. Identifying Fruit Mineral Removal Differences in Four Avocado Cultivars. Better Crop. Int.; 2001; 15, pp. 28-31.
23. Roy, R.N.; Finck, A.; Blair, G.J.; Tandon, H.L.S. Plant Nutrition for Food Security. A Guide for Integrated Nutrient Management; FAO Fertilizer and Plant Nutrition Bulletin FAO: Rome, Italy, 2006; 16.
24. Baligar, V.; Bennett, O. Outlook on Fertilizer Use Efficiency in the Tropics. Fertil. Res.; 1986; 10, pp. 83-96. [DOI: https://dx.doi.org/10.1007/BF01073907]
25. Alcaraz, M.L.; Thorp, T.G.; Hormaza, J.I. Phenological Growth Stages of Avocado (Persea americana) According to the BBCH Scale. Sci. Hortic.; 2013; 164, pp. 434-439. [DOI: https://dx.doi.org/10.1016/j.scienta.2013.09.051]
26. Shelia, J.M. Manual de Analisis de Suelos y Tejido Vegetal una Guia Teórica y Práctica de Metodologias; International Center for Tropical Agriculture- CIAT: Cali-Palmira, Colombia, 1993.
27. Escobar, J.V.; Cortes, M.; Correa, G.; Rondon, T.; Rodríguez, P. ‘Hass’ Avocado Internal Disorders under Simulated Export Conditions and Its Relationship with Flesh Mineral Content and Preharvest Variables. Horticulturae; 2021; 7, 77. [DOI: https://dx.doi.org/10.3390/horticulturae7040077]
28. Zasoski, R.J.; Burau, R.G. A Rapid Nitric-perchloric Acid Digestion Method for Multi-element Tissue Analysis. Commun. Soil Sci. Plant Anal.; 1977; 8, pp. 425-436. [DOI: https://dx.doi.org/10.1080/00103627709366735]
29. Watson, M.E.; Galliher, T.L. Comparison of Dumas and Kjeldahl Methods with Automatic Analyzers on Agricultural Samples under Routine Rapid Analysis Conditions. Commun. Soil Sci. Plant Anal.; 2001; 32, pp. 2007-2019. [DOI: https://dx.doi.org/10.1081/CSS-120000265]
30. Fonseca, A.; Lima, J.; Silva, S. Spacial Variability of Balanced Indexes of Kenworthy(BIK) for Macro and Micronutrients on the Coffee Canephora. J. Exp. Agric. Int.; 2018; 23, pp. 1-10. [DOI: https://dx.doi.org/10.9734/JEAI/2018/41211]
31. Food and Agriculture Organization of the United Nations. Codex Alimentarius Standard (Codex Stand 197, 1995); FAO: Rome, Italy, 1995; pp. 1-5.
32. Baquero Peñuela, J.E. Análisis e Interpretación de Resultados de Suelos; Corporación Colombiana de Investigación Agropecuaria—AGROSAVIA: Bogotá, Colombia, 1994.
33. Embleton, T.W.; Jones, W.W. Avocado and Mango Nutrition. Nutrition of Fruit Crops: Tropical, Subtropical, Temperate Tree and Small Fruits; Horticultural Publications, Rutgers State University: Piscataway, NJ, USA, 1966; pp. 51-76.
34. Bolan, N.S.; Hedley, M.J. Role of Carbon, Nitrogen, and Sulfur Cycles in Soil Acidification. Handbook of Soil Acidity; CRC Press: Boca Raton, FL, USA, 2003; pp. 43-70.
35. Ortiz, M.E.; Zapata, R.D.; Sadeghian, S.; Franco, H.F. Aluminio Intercambiable En Suelos Con Propiedades Ándicas y Su Relación Con La Toxicidad; Cenicafe: Manizales, Colombia, 2004.
36. Sánchez Espinosa, J.A.; Rubiano Sanabria, Y. Procesos Específicos de Formación En Andisoles, Alfisoles y Ultisoles En Colombia. Rev. EIA; 2015; 12, pp. 85-97.
37. Huggett, J.M. Clay Minerals. Reference Module in Earth Systems and Environmental Sciences; Elsevier: Amsterdam, The Netherlands, 2015.
38. Moorberg, C.J.; Crouse, D.A. Soils Laboratory Manual, k-State Edition; New Prairie Press: Manhattan, KS, USA, 2017.
39. Sparks, D.L. Environmental Soil Chemistry; Elsevier: Amsterdam, The Netherlands, 1995; ISBN 9780126564457
40. Bravo, R.I.; Campo, E.; Arboleda, C.A. Alófanos Causa de Indisponibilidad de Aniones en s Uelos del de Partame nto del Cauca-Colombia. Suelos Ecuatoriales; 2016; 46, pp. 13-30.
41. Roger, L. Allophane and Imogolite. Sedimentology; Springer: Berlin/Heidelberg, Germany, 1978; pp. 5-7.
42. Finch, H.J.S.; Samuel, A.M.; Lane, G.P.F. Fertilisers and Manures. Lockhart & Wiseman’s Crop Husbandry Including Grassland; Elsevier: Amsterdam, The Netherlands, 2014; pp. 63-91.
43. Mengel, K.; Kirkby, E.A. Principles of Plant Nutrition; 5th ed. Springer: Cham, Switzerland, 2001.
44. Chien, S.H.; Gearhart, M.M.; Collamer, D.J. The effect of different ammonical nitrogen sources on soil acidification. Soil Sci.; 2008; 173, pp. 544-551. [DOI: https://dx.doi.org/10.1097/SS.0b013e31817d9d17]
45. García, J.S.A.; Hurtado-Salazar, A.; Ceballos-Aguirre, N. Current Overview of Hass Avocado in Colombia. Challenges and Opportunities: A Review. Ciência Rural; 2021; 51, e20200903. [DOI: https://dx.doi.org/10.1590/0103-8478cr20200903]
46. Alcalá de Jesús, M.; Hidalgo Moreno, C.; Gutiérrez Castoreña, M. Mineralogía y Retención de Fosfatos En Andisoles. Terra Latinoamericana; 2009; 27, pp. 275-286.
47. Johan, P.D.; Ahmed, O.H.; Omar, L.; Hasbullah, N.A. Phosphorus Transformation in Soils Following Co-Application of Charcoal and Wood Ash. Agronomy; 2021; 11, 2010. [DOI: https://dx.doi.org/10.3390/agronomy11102010]
48. Shoji, S.; Takahashi, T. Environmental and Agricultural Significance of Volcanic Ash Soils. Glob. Environ. Res. Engl. Ed.; 2002; 6, pp. 113-135.
49. Monge, E.; Val, J.; Sanz, M.; Blanco, A.; Montañés, L. El Calcio Nutriente Para Las Plantas. Bitter Pit En Manzano. An. Estac. Exp. Aula Dei; 1994; 21, pp. 189-201.
50. Voogt, W. The growth of beefsteak tomato as affected by k/ca ratios in the nutrient solution. Acta Hortic; 1988; 222, pp. 155-166. [DOI: https://dx.doi.org/10.17660/ActaHortic.1988.222.18]
51. Bonomelli, C.; Gil, P.M.; Schaffer, B. Effect of Soil Type on Calcium Absorption and Partitioning in Young Avocado (Persea Americana Mill.) Trees. Agronomy; 2019; 9, 837. [DOI: https://dx.doi.org/10.3390/agronomy9120837]
52. Adams, F. Soil Acidity and Liming; American Society of Agronomy, Inc.: Madison, WI, USA, 1984.
53. Matimati, I.; Verboom, G.A.; Cramer, M.D. Nitrogen Regulation of Transpiration Controls Mass-Flow Acquisition of Nutrients. J. Exp. Bot.; 2014; 65, pp. 159-168. [DOI: https://dx.doi.org/10.1093/jxb/ert367]
54. Guerrero-Polanco, F.; Alejo-Santiago, G.; Sánchez Hernández, R.; Bugarín-Montoya, R.; Aburto-González, C.A.; Isiordia-Aquino, N. Respuesta Del Cultivo de Aguacate (Persea Americana Mill.) Variedad Hass a La Aplicación de Nitrato de Potasio. Acta Agron; 2018; 67, pp. 425-430. [DOI: https://dx.doi.org/10.15446/acag.v67n3.68858]
55. Lovatt, C.J.; Witney, G. Nitrogen Fertilization of the ‘Hass’ Avocado in California. AvoResearch; 2001; 1, pp. 1-4.
56. Lovatt, C.J. Nitrogen Nutrition of the ‘Hass’ Avocado: Where Does All the N Go?. Proceedings of the World Avocado Congress III; Tel Aviv, Israel, 22–27 October 1995; Volume 152, 159.
57. Harter, R.D. Acid Soils of the Tropics. ECHO Technical Note; ECHO: North Fort Myers, FL, USA, 2007; 11.
58. Fageria, N.K.; Nascente, A.S. Management of Soil Acidity of South American Soils for Sustainable Crop Production. Adv. Agron.; 2014; 128, pp. 221-275.
59. Almeida, J.A.; Torrent, J.; Barrón, V. Cor de Solo, Formas Do Fósforo e Adsorção de Fosfatos Em Latossolos Desenvolvidos de Basalto Do Extremo-Sul Do Brasil. Rev. Bras. Cienc. Solo; 2003; 27, pp. 985-1002. [DOI: https://dx.doi.org/10.1590/S0100-06832003000600003]
60. Johnson, S.E.; Loeppert, R.H. Role of Organic Acids in Phosphate Mobilization from Iron Oxide. Soil Sci. Soc. Am. J.; 2006; 70, pp. 222-234. [DOI: https://dx.doi.org/10.2136/sssaj2005.0012]
61. Bortoluzzi, E.C.; Pérez, C.A.S.; Ardisson, J.D.; Tiecher, T.; Caner, L. Occurrence of Iron and Aluminum Sesquioxides and Their Implications for the P Sorption in Subtropical Soils. Appl. Clay Sci.; 2015; 104, pp. 196-204. [DOI: https://dx.doi.org/10.1016/j.clay.2014.11.032]
62. Fink, J.R.; Inda, A.V.; Tiecher, T.; Barrón, V. Iron Oxides and Organic Matter on Soil Phosphorus Availability. Ciência e Agrotecnologia; 2016; 40, pp. 369-379. [DOI: https://dx.doi.org/10.1590/1413-70542016404023016]
63. Gérard, F. Clay Minerals, Iron/Aluminum Oxides, and Their Contribution to Phosphate Sorption in Soils—A Myth Revisited. Geoderma; 2016; 262, pp. 213-226. [DOI: https://dx.doi.org/10.1016/j.geoderma.2015.08.036]
64. van Breemen, N.; Driscoll, C.T.; Mulder, J. Acidic Deposition and Internal Proton Sources in Acidification of Soils and Waters. Nature; 1984; 307, pp. 599-604. [DOI: https://dx.doi.org/10.1038/307599a0]
65. Guppy, C.N.; Menzies, N.W.; Moody, P.W.; Blamey, F.P.C. Competitive Sorption Reactions between Phosphorus and Organic Matter in Soil: A Review. Soil Res.; 2005; 43, 189. [DOI: https://dx.doi.org/10.1071/SR04049]
66. Kreller, D.I.; Gibson, G.; Novak, W.; Van Loon, G.W.; Horton, J.H. Competitive Adsorption of Phosphate and Carboxylate with Natural Organic Matter on Hydrous Iron Oxides as Investigated by Chemical Force Microscopy. Colloids Surf. A Physicochem. Eng. Asp.; 2003; 212, pp. 249-264. [DOI: https://dx.doi.org/10.1016/S0927-7757(02)00325-4]
67. Cohen-Ofri, I.; Weiner, L.; Boaretto, E.; Mintz, G.; Weiner, S. Modern and Fossil Charcoal: Aspects of Structure and Diagenesis. J. Archaeol Sci.; 2006; 33, pp. 428-439. [DOI: https://dx.doi.org/10.1016/j.jas.2005.08.008]
68. Campbell, C.W.; Malo, S.E. A Survey of Avocado Cultivars. Proceedings of the First International Tropical Fruit Short Course: The Avocado; Fruit Crops Dept., Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida: Gainesville, FL, USA, 1976; pp. 20-24.
69. Campbell, J.L.; Rustad, L.E.; Boyer, E.W.; Christopher, S.F.; Driscoll, C.T.; Fernandez, I.J.; Groffman, P.M.; Houle, D.; Kiekbusch, J.; Magill, A.H. et al. Consequences of Climate Change for Biogeochemical Cycling in Forests of Northeastern North AmericaThis Article Is One of a Selection of Papers from NE Forests 2100: A Synthesis of Climate Change Impacts on Forests of the Northeastern US and Eastern Canada. Can. J. For. Res.; 2009; 39, pp. 264-284. [DOI: https://dx.doi.org/10.1139/X08-104]
70. Gómez-Guerrero, A.; Doane, T. The Response of Forest Ecosystems to Climate Change. Developments in Soil Science; Elsevier: Amsterdam, The Netherlands, 2018; pp. 185-206.
71. Salazar-García, S.; Cossio-Vargas, L.E.; González-Durán, I.J.L. La Fertilización de Sitio Específico Mejoró La Productividad Del Aguacate “Hass” En Huertos Sin Riego. Agricultura Técnica en México; 2009; 35, pp. 439-448.
72. Selladurai, R.; Awachare, C.M. Nutrient Management for Avocado (Persea americana Miller). J. Plant Nutr.; 2020; 43, pp. 138-147. [DOI: https://dx.doi.org/10.1080/01904167.2019.1659322]
73. Nuñez-Moreno, J.H.; Cortez-Flores, J.I.; Salazar-García, S.; Landois-Palencia, L.L. Evaluación Del Método DRIS Para Diagnosticar El Estado Nutrimental Del Aguacate (Persea americana Mill). Cv. Fuerte Agrociencia Ser. Agua-Suelo-Clima; 1991; 2, pp. 39-57.
74. Tamayo-Vélez, Á.D.; Bernal-Estrada, J.A.; Díaz-Díez, C.A.; Cano-Gallego, L.E.; Córdoba-Gaona, O.D. Effect of Rootstock/Scion Compatibility on Fruit and Foliar Nutrient Composition in Avocado (Persea americana Mill.) Cv. Hass in Colombia. Rev. Colomb. Cienc. Hortícolas; 2022; 16, e14833.
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Burbano-Diaz, Ronal Arturo