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Article

Intercropped Soybean Plant Population in a Coffee Plantation and Its Effects on Agronomic Parameters and Geospatial Information

by
Eberton de Carvalho
,
Gleice Aparecida de Assis
*,
George Deroco Martins
,
Douglas José Marques
,
Edson Aparecido dos Santos
,
Laura Cristina Moura Xavier
,
Lorrayne Maria Rodrigues Malta
and
Renan Zampiroli
Instituto de Ciências Agrárias, Universidade Federal de Uberlândia, Monte Carmelo 38500-000, MG, Brazil
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(2), 343; https://doi.org/10.3390/agronomy14020343
Submission received: 19 December 2023 / Revised: 18 January 2024 / Accepted: 26 January 2024 / Published: 7 February 2024
(This article belongs to the Section Innovative Cropping Systems)
Browse Figures
Versions Notes

Abstract

:
Coffee farming has high land use value, which can result in economic losses without proper land use planning. Intercropping has improved coffee production by providing an alternative income source to producers, especially in the crop formation phase. The objective of this study was to evaluate productivity, growth, and geospatial data in different soybean plant populations intercropped with coffee. The experiment was conducted at the Federal University of Uberlândia, in Monte Carmelo, Minas Gerais, Brazil. It had an experimental randomized block design with five treatments: the control (no soybeans); 80 thousand plants ha−1; 160 thousand plants ha−1; 240 thousand plants ha−1; and 320 thousand plants ha−1. Productive and vegetative characteristics of coffee and soybeans were evaluated, as well as the NDVI and GNDI vegetation indices. The mass of 1000 grains of soybean reached its peak (178.96 g) with a population of 222 thousand plants of soybean ha−1. The maximum soybean productivity of 102.78 bags ha−1 was obtained in the population of 185 thousand plants soybean ha−1. An increasing population of 240–320 thousand plants ha−1 soybean between coffee rows reduced soybean yield due to reduced light, causing plant lodging. The biometric parameters of the coffee trees did not change, which was evidenced by high normalized and green normalized difference vegetation indices (NDVI and GNDVI, respectively). Therefore, it is concluded that the use of soybeans between the coffee trees does not affect the vegetative and productive parameters of the coffee tree, making the use of the intercrop viable.

1. Introduction

Coffee is one of the most important agricultural crops in South American, African, and Asian countries, directly involving approximately 125 million people, with Brazil as the world’s largest producer and exporter [1]. Coffee production involves a large number of farms and workers [2]. Additionally, coffee is an agricultural commodity; thus, its production operating costs and value depend on several factors [3].
Central Cerrado is the main coffee-producing region in Brazil. On account of mechanization, seedlings are planted in rows spaced between 3.5 and 3.8 m on most farms. Coffee trees are tropical shrubs that grow slowly [4] and present low soil cover, especially in the first two years of the crop [5], resulting in underused areas that represent increased water and fertilizer use, weed growth [6,7,8], and soil loss through erosion.
In the first years of cultivation, underused coffee areas can be intercropped with crops that increase the cultivation value. The main objectives of cultivating different plants between coffee plantations are income diversification, optimized use of equipment and inputs, soil and water protection, nutrient cycling, biological control of diseases and pests, and weed control [9,10].
Fodder and agricultural crops intercropped with coffee improve soil fertility and increase producers’ income through crop diversification [9]. The low financial liquidity of coffee plants makes income diversification essential for coffee producers, and sowing to payback takes up to eight years [2,11]. Intercropping economic viability varies according to several factors, such as the market value of the additional crop. Thus, soybean can be an excellent alternative for intercropping in Cerrado areas with proper topography due to its value and market liquidity [12,13]. Soybean is the main Brazilian commodity and the main crop in the Cerrado region [14]. In addition, soybean fixates atmospheric nitrogen in the soil, presents a cycle of approximately 110 days, improves soil cover, and has a pivoting root system, which are positive characteristics for intercropping [15,16].
The soybean plant population must be defined at sowing. High population densities result in plants with thinner main stems, greater susceptibility to lodging [17], and fewer primary branches due to greater intra-specific competition between plants, with lower grain production per plant [18]. When plant density is higher, soybean plants compete mainly for light, leading to decreased photoassimilate availability for the vegetative growth of branches [19].
Considering that coffee trees are slow-growing plants that are overly sensitive to interference from other plants, intercropping can result in losses in case of competition between plants. Interference can harm the development of coffee plants; thus, maintaining the right spacing and plant population between rows is crucial. Young coffee plants spaced 3.0 m apart and intercropped with beans 1.0 m apart provided increased income for the producer, but plant competition decreased the yield [20]. Coffee trees respond differently to the interference of other plants. They can present decreased leaf area, low growth rate, lower productivity, smaller crown volume, and color pattern changes due to reduced water or nutrients [21,22,23]. Remote sensing and geoprocessing are accurate tools for qualitative and quantitative assessment of agricultural crops. Spectral indices, mapping, and remote evaluation have been successfully implemented in plant studies with spatial and temporal assessments [24].
Data on productivity, growth, and chlorophyll index of soybeans and coffee plants were collected in all treatments containing populations of soybean plants intercropped with coffee plants. This information was related to multispectral images, using the NDVI and GNDVI vegetation indices.
We believe that an increased soybean population intercropped with coffee results in different soybean and coffee agronomic parameters and crop geospatial information.

2. Materials and Methods

2.1. Experimental Area

This experiment was conducted in the experimental area of the Federal University of Uberlândia, in Monte Carmelo, MG, Brazil, with geographical coordinates 18°43′36″ S and 47°31′23″ W, at an altitude of 898 m (Figure 1).
The soil was chemically characterized in August 2021, before treatment differentiation, and in September 2022, after intercropping (Table 1). The soil in the experimental area is classified as clayey Red Latosol. Soil sampling was carried out with an auger at a depth of 0.20 m.

2.2. Coffee Planting and Management

Coffee trees of the IPR 100 cultivar were planted in March 2021, spaced 3.5 m between rows and 0.6 m between plants. The fertilizer used consisted of 50 g of P2O5 in simple superphosphate per hole (18% P2O5). Top dressing was applied 30 days after planting the seedlings, with 20 g of K2O per plant, divided into three applications per year, and 5 g of N per plant. This dose was repeated in the subsequent three months using the 22-00-22 formulation (N-P2O5-K2O).
A drip system was used for irrigation, with 0.6 m between drippers and at a flow rate of 2.1 L h−1.
Coffee trees were fertilized in the first year after planting with 142.86 kg of N ha−1, 20 kg of P2O5 ha−1, and 47.62 kg of K2O ha−1, using urea (45% N), simple superphosphate (18% P2O5), and potassium chloride (60% K2O) as sources, respectively.

2.3. Soybean Sowing and Fertilization

The soybean of the LG 60162IPRO cultivar (relative maturity 6.5) was sowed between the coffee tree rows in November 2021, spaced 0.4 m between the rows, with 14 seeds per linear meter without irrigation.
The standard fertilizer dose was 120 kg ha−1 year−1 of P2O5 and 40 kg ha−1 year−1 of K2O [25], based on soil analysis (Table 1) and considering an expected yield of 50–60 soybean bags ha−1, with no need for mineral N due to in-furrow inoculation with the bacterium Bradyrhizobium japonicum (840 mL ha−1 year−1). Mineral fertilizer sources were 00-18-00 simple superphosphate (N-P2O5-K2O) and 00-00-60 potassium chloride (N-P2O5-K2O), respectively, in the planting furrow, in November 2021.

2.4. Experimental Design and Treatments

The study had an experimental randomized block design with five treatments: the control (no soybeans); 80 thousand plants ha−1; 160 thousand plants ha−1; 240 thousand plants ha−1; and 320 thousand plants ha−1 (Figure 1). Each plot was 3 m long and 3.5 m wide and included five coffee plants. The three central plants of each plot were assessed, comprising a useful area of 6.3 m2 (1.8 m long and 3.5 m wide). Phytotechnical parameters were assessed in all soybean rows of each treatment.

2.5. Vegetative Parameter Assessment and Chlorophyll Index of Coffee and Soybean Plants

Growth was assessed monthly on both crops, from December 2021 to February 2022, totaling three assessments. Growth parameters assessed in coffee plants were crown diameter, plant height, stem diameter, number of internodes by plagiotropic branch, and chlorophyll index (CI) at 60 and 90 days after treatment differentiation.
The characteristics assessed in soybeans were plant height at 30, 60, and 90 days after emergence (DAE) and plant CI at 60 and 90 DAE.
CI was measured in the middle third of three soybean plants in the center line of each plot using a LEAF electronic chlorophyll meter. The plot value consisted of the mean CI of these three plants.
Each coffee tree was measured in the third or fourth pair of leaves of the plagiotropic branch located in the middle third of the coffee tree, on the upper side of useful plot planting lines, and the mean of the three plants represents the experimental plot. All the data were collected from 8 a.m. to ensure data sampling homogeneity.

2.6. Assessment of Coffee and Soybean Production Parameters

Soybean was harvested in March 2022 and February 2023. Yield was measured by reaping, mechanically threshing, and obtaining the grain mass of all plants in the useful area of each plot. The data were subsequently converted into kg ha−1. Mass was determined from a sample of 1000 grains from each experimental unit. Yield was expressed in bags ha−1, with grain moisture corrected to 13% (wet basis) and grain mass to 1000 grains, in grams.
The first coffee was harvested in August 2023, by stripping the cherries from the three useful plants in the plot. Harvesting began when the percentage of green cherries in the experimental area was reduced to less than 20%. The volume produced at each plot was determined, and a 10 L sample was separated and dried in a drying patio. Coffee beans’ mass and volume were determined after reaching 11% moisture. Subsequently, the samples were processed and the mass, volume, and moisture of the coffee beans were determined again. The ratio of the volume of a 10 L sample of harvested coffee and the mass of the processed sample were used to determine the production per plot and were then extrapolated to productivity in bags ha−1. These data were used to calculate the yield (L of “farm coffee” used to fill up a 60 kg bag of processed coffee) [26].

2.7. Spatial Distribution Mapping of Agronomic Parameters in Coffee and Soybeans

Spatial distribution parameters measured in soybeans and coffee trees were mapped using ordinary kriging interpolation in the SmartMap plugin software (Smart Map plugin version 1.3.3).
The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) were used to relate the vegetative growth of coffee trees in each treatment to multispectral images.
Sample pre-processing used to support points represented by experiment ends. These images were acquired using a Mapir high-resolution camera (NDVI images) coupled with an ARP Phanton4 drone, which collects red, green, and blue (RGB) bands (green, 550 nm; red, 660 nm; and near-infrared, 850 nm), and flies 50 m above the ground, with a ground sample distance (GSD) of 1.1 cm, providing good spatial resolution images [27]. The images were captured at stage R1 (7 December 2021), because plants at this stage of development are more sensitive to spectral responses [28].

2.8. Statistical Analysis

After meeting normality residual assumptions by the Jarque–Bera test and homoscedasticity by the Levene’s test, the data were subjected to analysis of variance by the F test at 5% probability. Plant population factor means were fitted to regression models for soybean plant height and yield, and thousand grain mass when significant differences were detected between treatments. Other soybean and coffee growth characteristics and the CI were analyzed using the time split-plot scheme, and treatment means were compared using the Tukey’s test at a 5% probability. The SPEED Stat software (SPEED Stat software 3.2) was used for statistical analyses [29].

3. Results

3.1. Coffee Tree Growth and Productivity Parameters

Statistical analysis showed little variability in coffee tree growth parameters between treatments. Plant height showed a significant treatment effect at 5% probability (p-value = 0.04), and a regression model was fitted. Coffee stem and crown diameter, number of internodes, and CI showed no significant differences related to the population of soybean plants grown between coffee tree rows (Table 2).
The coffee trees presented a mean stem diameter of 1.22 cm, a crown diameter of 45.56 cm, 7.6 internodes per productive branch, and a CI of 67.18. Therefore, the increased soybean population between coffee tree rows did not affect these vegetative growth parameters.
Plant height resulted in a quadratic polynomial model (R2 = 69.51%), with coffee trees reaching a maximum height of 56.85 cm with a population of 177 thousand plants soybean ha−1 between rows (Figure 2B).
Coffee plant production parameters showed no significant differences between treatments for the productivity of the first coffee crop (p-value = 0.95) and coffee yield (p-value = 0.87). Coffee trees had a mean yield of 30.57 bags ha−1 and a yield of 345.95 L of coffee harvested from a plant to fill up a 60 kg bag of processed coffee due to the high percentage of beans (53.8%) at harvest time (Table 3).

3.2. Soybean Growth and Productivity Parameters

As for the effects of intercropping on soybean biometric parameters, plant height was highly significant (p-value < 0.01), fitting a quadratic model (Figure 2A). The plants reached a maximum height of 77.02 cm at a density of 265 thousand plants soybean ha−1, decreasing with higher densities due to plant lodging.
There was no influence of the soybean plant population on the CI, even in plots where the distance between the soybean and the coffee plants was 0.35 m at the time of sowing. The mean CI values were 44.01 and 33.02 units, respectively, at 60 and 90 DAE (Table 4).
As for soybean yield parameters, the plant population of soybeans intercropped with coffee trees influenced the mass of 1000 grains and the mean yield in the 2021/22 and 2022/23 crops (p-value < 0.05).
The mass of 1000 grains reached its peak (178.96 g) with a population of 222 thousand plants of soybean ha−1 (Figure 3A).
As for grain yield, a quadratic polynomial model was fitted (R2 = 77.0%), showing a maximum yield of 102.78 bags ha−1 at a population of 185 thousand plants soybean ha−1 (Figure 3B), with a decrease from this point onwards, possibly due to greater competition with the coffee tree and the lodging of the soybeans.

3.3. Agronomic Parameters Spatial Distribution Maps

Figure 4 and Figure 5 show spatial distributions of the agronomic parameters measured in the soybean crop (height, chlorophyll, and yield) and the coffee tree (height, stem diameter, crown diameter, number of internodes, CI, and yield).
As for the soybean plant height parameter, the areas on the left side of the plot had higher values and consequently higher yields throughout the development of the crop. Chlorophyll, on the other hand, had higher values in the lower part of the plot.
All the parameters measured in the coffee crop showed great variability of low and high values, not characterizing areas of concentration, except the productivity, which was higher in the upper portion of the plot (Figure 5).

3.4. Spatial Distribution of Plant Vigor

Figure 6A,B show that the lower part of the study area has GNDVI and NDVI values higher than 0.5, which means high vegetative vigor. On the other hand, in the upper part, the index values are higher than 0.5 for the coffee crop, while for soybeans they are lower than 0.5. Thus, the experiment shows that a population of soybean plants intercropped with the coffee crop only influences the soybean, i.e., intercropping did not negatively affect the vegetative vigor of the coffee plants.

4. Discussion

Increasing the soybean population between rows of coffee trees did not affect the CI of the coffee tree (C. arabica). Opposite results were reported by Fialho et al. [30], who found reductions in nitrogen concentrations in coffee tree leaves in coexistence with grasses. The competition of soybeans on the coffee crop when four rows are used is mainly due to the fact that C. arabica L. is in its formative stage and is extremely sensitive to light, water, and nutrient limitations. In the treatments with 240 and 320 thousand plants soybean ha−1, a tendency for competition with the perennial crop was observed, since it grows more slowly than soybeans.
The intercropping of coffee trees with annual crops such as corn and beans stood out in the implementation phase of coffee crops, mainly due to the diversification of agricultural products in the area and the cost/benefit ratio [31]. However, competition may occur according to the species and growth habits of the crops that are intercropped with the coffee tree.
One of the possible factors that may have contributed to the non-significance of the treatments in the production parameters of the coffee tree, even with the use of 320 thousand plants soybean ha−1 between coffee tree rows, refers to the benefits promoted by the use of a Fabaceae in the intercropping system. The use of this kind of plant results in nitrogen fixation and soil protection [32], reducing the incidence of weeds [33] and increasing soil moisture, especially near the projection of the coffee tree canopy. Although plant height was affected by the use of a population greater than 240 thousand soybean plants ha−1, this variable did not influence productivity and yield. In addition, as this is a perennial crop with biennial production, the use of intercropping and the influence of the number of soybean rows planted between the rows of coffee trees can be seen in the long term.
An experiment conducted with soybean cultivars with different populations between the rows of coffee trees showed that the presence of intercropping soybeans reduced the yield of coffee beans as the number of rows increased when using the Paranaíba and IAC-8 cultivars [34]. However, no differences were observed in the yield of coffee trees with the Doko cultivar, which has a late cycle.
The use of tree species intercropped with coffee showed a mean increase in productivity of 34 bags ha−1 of coffee intercropped with African mahogany (Khaya ivorensis), regardless of the spacing between the species, as well as superior sensory quality [35]. Increased productivity depends on the interaction between the intercropped species. Moreover, there was a five-fold increase in land use efficiency with the intercropping of irrigated Arabica coffee and Madam walnut compared to the means for monocropping crops under rainfed conditions [36].
In addition to the potential benefits of increased productivity, the use of cover crops in coffee plantations promotes greater plant diversification and consequently increases the number of natural enemies of the key pests of arabica coffee [37].
All treatments in the soybean crop showed a higher CI at 60 DAE compared to 90 DAE, due to the transition from stage R5 (start of grain filling) to R6 (green grain or full pod) in soybeans (Table 3). These results corroborate those obtained by [38], who found a marked reduction in the SPAD index at all plant densities from R5.4 onwards, due to the increase in the speed of leaf maturation in the final phase of the soybean cycle, with the remaining leaves senescing as a result of the redistribution of nutrients and photoassimilates to the grains [39].
In the population with 320 thousand plants soybean ha−1 between the rows of coffee trees, the soybean was harmed by the intense shading produced by C. arabica. Thus, despite the lower initial population, the use of 80–160 thousand plants showed higher yields compared to the other treatments due to the lower competition with coffee trees and the higher percentage of plant survival, corroborating the results obtained by Rezende et al. [34].
In a bean production system (Phaseolus vulgaris L.) in the same family as soybean, intercropped with newly planted coffee trees, the bean yield increased, but the coffee tree’s stem diameter decreased, with a tendency for increased plant mortality from the use of 240 thousand plants ha−1 [40]. The present study showed divergent results in terms of soybean yield. This result can be explained by the fact that intercropping was established in the area eight months after the coffee plantation, increasing competition with the soybean due to the shading promoted by the perennial crop, reducing grain yield as the soybean plant population increased.
Spatial distribution of the measured agronomic parameters showed that there is no correlation between the treatments and the observations made on the coffee tree, confirming that the soybean plant population does not influence coffee plant productivity.
The image monitoring on soybeans showed that the high index values in the lower part of the area may be associated with a less advanced stage of senescence compared to the treatments located in the upper block, or also possibly due to the greater soil moisture in the areas located in the lower areas of the plot, providing greater plant vigor (Figure 6). On the other hand, the high values for the coffee crop throughout the area may be associated with the fact that soybeans provided a great advantage in terms of biological nitrogen fixation, making a significant contribution to plant vigor [41,42]. The low variability of the index values for the coffee crop also indicates that the soybean intercropping did not result in morphophysiological changes in the coffee plant.

5. Conclusions

The results of the present study showed that the presence of soybean plants between rows had no effect on coffee productivity. The population between 80 and 150 thousand plants of soybean ha−1 presented the highest yield. Increasing the population from 240 to 320 thousand plants ha−1 and soybean plants grown between coffee tree rows reduced soybean yields due to the lack of light, causing plant lodging. The biometric parameters of the coffee trees, such as height, stem diameter, cup diameter, and internodes, did not change, which was evidenced by high NDVI and GNDVI vegetation indices.

Author Contributions

Conceptualization, G.A.d.A., D.J.M., E.A.d.S. and G.D.M.; methodology, E.d.C., R.Z. and G.A.d.A.; software, G.D.M., R.Z. and L.C.M.X.; validation, E.d.C. and L.M.R.M.; formal analysis, D.J.M. and E.A.d.S.; investigation, G.A.d.A. and G.D.M.; resources, D.J.M. and E.A.d.S.; data curation, E.d.C.; writing—G.A.d.A., D.J.M., E.A.d.S. and G.D.M.; visualization, G.A.d.A.; supervision, G.A.d.A.; project administration, G.A.d.A.; funding acquisition, G.A.d.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received the Federal University of Uberlândia, Brazil.

Data Availability Statement

The datasets generated during and/or analyzed during the present study are not publicly available since, despite being anonymized, they portray a single farm, so publicly sharing the data can be experienced as sensitive for the case study participants. Datasets are available from the corresponding author upon reasonable request.

Acknowledgments

We thank the Universidade Federal de Uberlândia for the assistance and training provided to the authors.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 14 00343 g001
Figure 1. Experimental area sketch and definition of the crops and study area. In (A) The red box indicates the location of the city of Monte Carmelo in the state of Minas Gerais—Brazil. In (B) the red box indicates the location of the experimental area in the city of Monte Carmelo. In (C) the red box indicates the delimitation of the experimental area.
Figure 1. Experimental area sketch and definition of the crops and study area. In (A) The red box indicates the location of the city of Monte Carmelo in the state of Minas Gerais—Brazil. In (B) the red box indicates the location of the experimental area in the city of Monte Carmelo. In (C) the red box indicates the delimitation of the experimental area.
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Agronomy 14 00343 g002
Figure 2. Soybean plant (A) and coffee tree (B) height as a function of plant population in an intercropped system with coffee trees (mean of two years in the 21/22 and 22/23 crops).
Figure 2. Soybean plant (A) and coffee tree (B) height as a function of plant population in an intercropped system with coffee trees (mean of two years in the 21/22 and 22/23 crops).
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Figure 3. Mass of 1000 grains (A) and soybean yield (B) as a function of plant population in a system intercropped with coffee (mean of two years in the 21/22 and 22/23crops).
Figure 3. Mass of 1000 grains (A) and soybean yield (B) as a function of plant population in a system intercropped with coffee (mean of two years in the 21/22 and 22/23crops).
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Figure 4. Spatial distribution of height (30, 60, and 90 days), chlorophyll index (30 days and 90 days), and productivity of soybean intercropped with coffee.
Figure 4. Spatial distribution of height (30, 60, and 90 days), chlorophyll index (30 days and 90 days), and productivity of soybean intercropped with coffee.
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Figure 5. Spatial distribution of height (30, 60, and 90 days), stem diameter (30, 60, and 90 days), cup diameter (30, 60, and 90 days), internodes (30, 60, and 90 days), chlorophyll index (30 days and 90 days), and productivity of coffee trees in an intercropping system.
Figure 5. Spatial distribution of height (30, 60, and 90 days), stem diameter (30, 60, and 90 days), cup diameter (30, 60, and 90 days), internodes (30, 60, and 90 days), chlorophyll index (30 days and 90 days), and productivity of coffee trees in an intercropping system.
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Figure 6. NDVI (A) and GNDVI (B) at stage R1 of intercropped soybean–coffee in each treatment regarding the population density of soybean intercropped with coffee.
Figure 6. NDVI (A) and GNDVI (B) at stage R1 of intercropped soybean–coffee in each treatment regarding the population density of soybean intercropped with coffee.
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Table 1. Chemical and physical fertility analysis of the soil before sowing (1) and chemical analysis of the soil after coffee and soybean intercropping (2).
Table 1. Chemical and physical fertility analysis of the soil before sowing (1) and chemical analysis of the soil after coffee and soybean intercropping (2).
Chemical Analysis
ElementsUnit(1)(2)
pH (H2O)1:2.56.505.40
pH (CaCl2)1:2.55.70-
P meh.mg dm−36.8019.66
K+mg dm−3135.5180.95
S-SO42mg dm−310.314.90
K+cmolc dm−30.350.46
Ca2+cmolc dm−32.523.63
Mg2+cmolc dm−30.761.23
Al3+cmolc dm−30.000.03
H + Alcmolc dm−31.603.31
SBcmolc dm−33.635.32
Tcmolc dm−35.238.63
tcmolc dm−33.635.35
V%69.3061.64
m%0.000.56
MOdag kg−14.943.70
COdag kg−12.87-
Bmg dm−30.370.71
Cumg dm−32.403.70
Femg dm−322.0026.92
Mnmg dm−33.3016.36
Znmg dm−32.3010.96
Physical analysis (1)
Areiag kg−129.57
Silteg kg−122.67
Argilag kg−147.76
P, K+ = Mehlich-1; S-SO42− = calcium phosphate monobasic 0.01 mol L−1; Ca2+, Mg2+, Al3+ = KCl 1 mol L−1 method; H + Al = [buffer solution SMP pH 7.5]; B = [BaCl2. 2H2O 0.125% with hot water]; Cu, Fe, Mn, Zn = DTPA.
Table 2. Mean stem diameter (cm), crown diameter (cm), number of internodes on the plagiotropic branch, and CI of coffee trees as a function of the population of soybean plants grown between rows of Coffea arabica L.
Table 2. Mean stem diameter (cm), crown diameter (cm), number of internodes on the plagiotropic branch, and CI of coffee trees as a function of the population of soybean plants grown between rows of Coffea arabica L.
Soybean Population (Thousand Plants ha−1)Stem
Diameter (cm)		Crown
Diameter (cm)		Number of
Internodes		Chlorophyll
Index
01.20 a43.43 a7.28 a66.53 a
801.21 a45.70 a7.60 a67.80 a
1601.29 a46.76 a8.14 a70.37 a
2401.19 a46.74 a7.29 a65.32 a
3201.19 a45.17 a7.68 a65.91 a
F *0.721.300.392.52
CV1 ** (%)17.2510.6129.536.51
CV2 ** (%)30.9212.9910.789.66
* Snedecor’s F distribution value. ** Coefficient of variation (%). Means followed by the same letter in the column do not differ by the F test at 5% significance.
Table 3. Mean productivity (bags ha−1) and yield (L of coffee harvested from the plant to obtain a 60 kg bag) of coffee trees as a function of the population of soybean plants grown between rows of Coffea arabica L.
Table 3. Mean productivity (bags ha−1) and yield (L of coffee harvested from the plant to obtain a 60 kg bag) of coffee trees as a function of the population of soybean plants grown between rows of Coffea arabica L.
Soybean Population
(Thousand Plants ha−1)		Productivity (Bags ha−1)Yield (L)
030.04 a350.94 a
8026.68 a325.99 a
16032.54 a377.06 a
24033.87 a334.76 a
32029.72 a341.00 a
F *0.170.30
CV ** (%)42.1820.51
* Snedecor’s F distribution value. ** Coefficient of variation (%). Means followed by the same letter in the column do not differ by the F test at 5% significance.
Table 4. Mean CI of soybean plants as a function of the soybean population intercropped with Coffea arabica L. 60 and 90 DAE.
Table 4. Mean CI of soybean plants as a function of the soybean population intercropped with Coffea arabica L. 60 and 90 DAE.
Soybean Population
(Thousand Plants ha−1)		Chlorophyll Index 60 DAEChlorophyll Index 90 DAE
042.7 aA35.1 aB
8043.8 aA34.3 aB
16044.8 aA31.3 aB
24045.0 aA31.4 aB
F *4.87
CV1 ** (%)6.23
CV2 ** (%)5.02
* Snedecor’s F distribution value. ** Coefficient of variation (%). Means followed by the same uppercase letter in the row and lowercase letter in the column do not differ by the F Test at the 5% significance level.
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MDPI and ACS Style

de Carvalho, E.; de Assis, G.A.; Martins, G.D.; Marques, D.J.; dos Santos, E.A.; Xavier, L.C.M.; Malta, L.M.R.; Zampiroli, R. Intercropped Soybean Plant Population in a Coffee Plantation and Its Effects on Agronomic Parameters and Geospatial Information. Agronomy 2024, 14, 343. https://doi.org/10.3390/agronomy14020343

AMA Style

de Carvalho E, de Assis GA, Martins GD, Marques DJ, dos Santos EA, Xavier LCM, Malta LMR, Zampiroli R. Intercropped Soybean Plant Population in a Coffee Plantation and Its Effects on Agronomic Parameters and Geospatial Information. Agronomy. 2024; 14(2):343. https://doi.org/10.3390/agronomy14020343

Chicago/Turabian Style

de Carvalho, Eberton, Gleice Aparecida de Assis, George Deroco Martins, Douglas José Marques, Edson Aparecido dos Santos, Laura Cristina Moura Xavier, Lorrayne Maria Rodrigues Malta, and Renan Zampiroli. 2024. "Intercropped Soybean Plant Population in a Coffee Plantation and Its Effects on Agronomic Parameters and Geospatial Information" Agronomy 14, no. 2: 343. https://doi.org/10.3390/agronomy14020343

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