Next Article in Journal
Characterization of Antagonist Potential of Selected Compost Bacterial Isolates (CBI) against Plant and Human Pathogens
Previous Article in Journal
Genome-Wide Identification and Expression Analysis of BrATGs and Their Different Roles in Response to Abiotic Stresses in Chinese Cabbage
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 12
Issue 12
10.3390/agronomy12122974
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Previous Fall–Winter Crop on Spring–Summer Soybean Nutrition and Seed Yield under No-Till System

by
Rogério P. Soratto
1,*,
Fernando V. C. Guidorizzi
1,2,
Westefann S. Sousa
1,
Amanda P. Gilabel
1,2,
André L. G. Job
1,3 and
Juliano C. Calonego
1
1
Department of Crop Science, College of Agricultural Sciences, São Paulo State University (UNESP), Av. Universitária, 3780, Lageado Experimental Farm, Botucatu 18610-034, SP, Brazil
2
ICL América do Sul, São Paulo 01000-000, SP, Brazil
3
McCain Brasil, Araxá 8180-000, MG, Brazil
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(12), 2974; https://doi.org/10.3390/agronomy12122974
Submission received: 14 October 2022 / Revised: 20 November 2022 / Accepted: 23 November 2022 / Published: 26 November 2022
(This article belongs to the Section Farming Sustainability)
Browse Figures
Versions Notes

Abstract

:
Interest in fall–winter species options for rotation with soybean (Glycine max (L.) Merr.) has arisen; however, little is known about how they can affect the performance of subsequent soybean under a no-tillage system in tropical environments. Our objective was to evaluate the leaf nutrient concentration, aboveground dry matter (DM) accumulation, macronutrient uptake, yield components, and seed yield of soybean cropped in succession to different crop species. Consequently, a field experiment was conducted during three consecutive growing seasons in Botucatu, São Paulo State, southeastern Brazil. The experiment was arranged in a randomized complete block design with four replicates. The treatments consisted of the cultivation of five crops (crambe (Crambe abyssinica Hochst. ex. R.E. Fries), maize (Zea mays L.), safflower (Carthamus tinctorius L.), sorghum (Sorghum bicolor (L.) Moench), and sunflower (Helianthus annuus L.)) in rotation with soybean, in addition to plots that lie fallow (spontaneous weeds) in the soybean off-season, totaling six treatments. Letting plots lie fallow during the off-season reduced the DM accumulation, nutrient uptake, and seed yield of the soybean crop in succession. Preceding cultivation of fall–winter crambe or sunflower favored the uptake of P, K, Ca, Mg, and S by the following soybean crop. The cultivation of sorghum, safflower, and crambe as fall–winter crops also increased the seed yield of subsequent soybean (from 12 to 18% on the average of three growing seasons) compared to fallow plots. The highest increases in soybean seed yield were found in succession to maize (37%) or sunflower (45%) in the second and third growing seasons, respectively.

1. Introduction

Soybean (Glycine max (L.) Merr.) is one of the most economically important crops in the world, with a production of approximately 353 million tons in 2020 [1]. It is a staple protein source in most Asian countries, and also an oilseed crop used as a feedstock for edible oil and biofuels. Furthermore, the bran, a by-product of oil extraction, serves as an important feed for livestock and aquaculture [2]. Brazil is the world’s largest soybean producer, accounting for approximately 1/3 of the world’s production [1,3]. Other major soybean-producing countries are the USA, Argentina, and China.
Given the economic importance and versatility of the soybean crop, there has been an immense demand for strategies to increase its productivity. Among the main soil management practices or systems in which soybeans are cultivated in Brazil, the no-tillage (NT) system, which is characterized by reduced soil disturbance, crop rotation, and maintenance of crop residues on the soil surface, has recently gained attention [4,5,6,7,8]. The NT system is considered one of the main pillars of conservation agriculture in tropical regions [7], because soils managed under NT systems have improved physical [4,9,10], chemical, and biological properties [5,11,12]. In Brazil, the area managed under NT systems currently exceeds 33 million ha, and its expansion in the last decades is strongly associated with the increase of the area under soybean [7].
In soils managed under NT systems, crop rotation and the maintenance of plant residues covering the soil are critical to avoid soil loss by erosion and improve nutrient cycling to deeper soil layers [8,13]. Thus, crop rotation has also been shown to have an impact on cropping system performance [14,15,16]. In Minnesota and Wisconsin, soybean yielded 5 to 16% more seed when grown in rotation with corn than when it was grown continuously [14]. In Nebraska, soybean seed yield in soybean–maize rotation was greater than the continuous soybean system in 67% of the growing season, and similar in the remaining years [16]. In Jilin Province, China, soybean–maize rotation produced a better yield and profitability, particularly in dry years, than the continuous maize system [17]. In Brazil, soybeans are mainly sown in the spring (October–December), and they are rotated with a second (or fall–winter) cash crop, such as maize (Zea mays L.), sorghum (Sorghum bicolor (L.) Moench), or sunflower (Helianthus annuus L.), in double-crop systems in the central-west and southeast macro-regions of the country. However, reduced water availability hampers the success of these cash crops in the fall–winter season in many regions, especially when sowing is carried out later (e.g., from March onwards) [13,18]. Furthermore, second crop maize yield has been drastically reduced by the corn stunt disease complex, transmitted by the corn leafhopper Dalbulus maidis (DeLong & Wolcott), in recent years [19]. Consequently, cash crops, such as crambe (Crambe abyssinica Hochst. ex. R.E. Fries) and safflower (Carthamus tinctorius L.) are under consideration as [18,20].
Thus, the important aspects to consider when selecting the species to be included in the NT system rotation scheme are that it should be adaptable to the soil and climate, show rapid establishment and have a vigorous root system, produce ample and persistent residues, cycle nutrients, provide the possibility of additional income, and not be susceptible to pests and diseases [6,13,15,21,22], but mainly that the species has a positive effect on the performance of soybean crop in succession. It was hypothesized that fall–winter crop species may affect differently the seed yield of soybean in rotation, mainly interfering with crop nutrition. Therefore, our objective was to evaluate the nutrition, aboveground dry matter (DM) accumulation, macronutrient uptake, yield components, and seed yield of soybean cropped in succession to different crop species during three growing seasons.

2. Materials and Methods

2.1. Site Description

The experiment was conducted during the growing seasons of 2014–2017 in Botucatu, São Paulo State, southeastern Brazil (48°26′ W, 22°51′ S, 740 m asl). The region has a Cwa climate (according to the Köppen classification system), meaning tropical, with a dry winter and a hot and rainy summer. During the experimental period, rainfall and temperatures were measured daily and compared to long-term (50-yr) annual averages (Figure 1).
The soil was classified as a clay-textured Ferrasol [23]. In March 2014, prior to initiating the experiment, soil samples were obtained from a soil depth of 0- to 0.2-m. Subsequently, the chemical characteristics of the soil were determined according to the methods described by van Raij et al. [24], and soil texture was determined by pipette method [25]. Soil chemical characteristics were a pH(CaCl2) of 4.7, organic matter of 31 g dm−3, and resin-extractable P of 22 mg dm−3. The exchangeable K, Ca, Mg, H + Al, and cation exchange capacity were 6.4, 37.7, 14.3, 64, and 122.4 mmolc dm−3, respectively, whereas the base saturation was 47%. Furthermore, the sand, silt, and clay contents were 254, 157, and 589 g kg−1, respectively.

2.2. Experimental Design, Treatments, and Crop Management

The experiment was arranged in a randomized complete block design with four replicates. The treatments consisted of the cultivation of five crops (crambe, maize, safflower, sorghum, and sunflower) in rotation with soybean, as well as plots that remained fallow (spontaneous weeds) in the soybean off-season, totaling six treatments. Each plot had a dimension of 5 by 11 m (55 m2), and the usable area had a dimension of 4 by 10 m (40 m2) because the 0.5 m at each border was not considered for evaluations.
Crambe (cv. FMS Brilhante), maize (cv. P3456), safflower (cv. IMA 2334), sorghum (cv. P8419), and sunflower (cv. H360) were mechanically sown using a tractor-driven multiple NT seeder (Table 1). Fertilization as a N-P2O5-K2O-formulated fertilizer was applied to all plots at the same rate, in all growing seasons, with the exception of the plots that were allowed to lie fallow during this period. In the three growing seasons, crambe, maize, safflower, sorghum, and sunflower were cultivated until the seed harvest. Spontaneous weeds present in the plots were terminated with glyphosate (N-(phosphonomethyl) glycine) application (1.5 kg a.i. ha−1) using a spray volume of 200 L ha−1 (Table 1). Soybean was sown at a density of 13–17 seeds m−1 and with 0.5 m spacing between the rows, using a tractor-driven multiple NT seeder (Table 1). Soybean seeds were treated with the fungicides carboxin (5,6-dihydro-2-methyl-N-phenyl-1,4-oxathiin-3-carboxamide) and thiram (tetramethyl thiuram disulfide) at a rate of 50 + 50 g a.i. 100 kg of seeds−1. Additionally, the crop was treated with the insecticide thiamethoxam (3-(2-chloro-1,3-thiazol-5-ylmethyl)-5-methyl-1,3,5-oxadiazinan-4-ylidene(nitro)amine; 50 g a.i. 100 kg of seeds−1), and with inoculant containing strains of Bradyrhizobium japonicum. Weeds were controlled in all plots by herbicide application in early soybean growth stages.

2.3. Plant Sampling, Measurements, and Analyses

At the flowering of soybean (R2 stage [26], equivalent to BBCH growth stage 61 [27]), 15 fourth trifoliate leaves fully expanded from the apex of the plant were taken from 15 random plants in each plot for leaf diagnosis [28]. The aboveground portion of plants was collected at the beginning of maturity (R7 stage/BBCH 93) for determinations of aboveground DM accumulation and nutrient uptake. For this purpose, a set of eight plants in a row was collected per plot. Plant tissues were arranged into paper bags and dried in an oven with forced-air circulation at 65 °C for 72 h. Thereafter, the dry aboveground portion of plants was weighed, and the per-plant DM accumulation was determined. The per-unit area DM production was calculated by multiplying the per-plant DM accumulation by plant population.
Dry plant tissues were ground (to pass through a 40-mesh stainless steel screen) and subjected to nutrient concentration determinations. The N concentration of plant tissues was determined by H2SO4 (sulfuric acid) digestion and quantified using the semi-micro-Kjeldahl method [29]. For the determination of other nutrients, the samples were digested with a HNO3 (nitric acid)–HClO4 (perchloric acid) solution. From the digested solution, P and S were determined by spectrophotometry, and K, Ca, and Mg were determined by atomic absorption spectrophotometry [29]. The nutrient uptake was calculated by multiplying the nutrient concentration by the accumulated DM in the aboveground portion of plants, as well as considering final plant population.
Soybeans were harvested at full maturity (R8 stage/BBCH 99). The seed yield and yield components (final population of plants, number of pods per plant, number of seeds per pod, and 1000-seed weight) were determined at harvest. The final plant population was determined by counting the plants contained in four 6-m-long rows within the usable area of each plot. The seed yield was determined by mechanically harvesting plants in the same four 6-m-long rows using a research plot harvester. Seeds were weighed, and the yield was calculated at a moisture of 130 g kg−1. The number of pods per plant and the number of seeds per pod were evaluated in a set of 10 plants collected in one of the remaining rows. The 1000-seed weight was determined by randomly collecting and weighing four samples per plot, and the values were adjusted to a moisture content of 130 g kg−1.

2.4. Statistical Analyses

Data were individually subjected to analysis of variance (ANOVA) using the SISVAR statistical software package [30]. Prior to analysis, the data were inspected for homogeneity and normality. Growing seasons and treatment (previous crops) were the fixed effects, while blocks and their interactions were considered random effects. Means of treatments were compared by Tukey’s test (p < 0.05). All growing seasons and variables of aboveground nutrient concentration, nutrient uptake, per-unit area DM, yield components, and seed yield were used to process a principal component analysis (PCA) using R software version 4.2.1 [31]. Analysis was used to explore similarities between the treatments and variables evaluated.

3. Results

3.1. Leaf Nutrient Concentrations

The ANOVA for the leaf diagnosis revealed that N concentrations in soybean leaves were not affected by the growing season or by the interaction between the growing season and the previous crop, but only by the previous crop (Table 2).
The highest values were observed when soybean was cultivated after crambe, which differs significantly from the lowest values observed for maize (Table 2). However, the values of all the other crops were very similar and did not differ significantly from each other. Leaf concentrations of P, Ca, and Mg were influenced only by the growing season, whereas leaf S concentration was affected by both factors when considered singly. The concentrations of P and S in leaves were higher in the 2014–2015 and 2016–2017 growing seasons, while the highest concentrations of Ca and Mg occurred in the 2015–2016 growing season. Regardless of the growing season, the previous crop of grasses (maize and sorghum) in the fall–winter period resulted in the highest leaf S concentrations, differing significantly from the treatment with preceding cultivation of safflower.
The K concentration in soybean leaves was affected by both the growing season and the growing season × previous crop interaction in all treatments, except for soybean grown in succession to sorghum, in which there was no difference between growing seasons (Table 2). The highest concentrations of K in soybean leaves were observed in the 2015–2016 growing season and the lowest in 2016–2017 (Figure 2A). The previous crop only affected leaf K concentration in the 2016–2017 growing season, with the highest values obtained when soybean was grown following sorghum.

3.2. Aboveground Dry Matter

The per-plant and per-unit area aboveground DM biomass of the soybean plants were affected by the growing season, previous crop, and their interaction (Table 2). In the 2014–2015 growing season, the highest per-plant DM accumulation was observed in soybean plants cultivated following sunflower (33.0 g plant−1), differing from treatments with the preceding cultivation of sorghum (24.1 g plant−1) and allowing the plots to lie fallow (27.0 g plant−1; Figure 2B). However, in 2015–2016, the highest per-plant DM accumulation was a result of the preceding cultivation of crambe and maize (48.0 and 44.9 g plant−1), while the lowest accumulation was observed as a result of the previous cultivation of sorghum or leaving the plots to lie fallow (27.4 and 33.0 g plant−1, respectively). In 2016–2017, the highest and lowest per-pant soybean DM accumulations were also provided by the preceding cultivation of crambe (28.6 g plant−1) and sorghum (22.9 g plant−1), respectively. When soybean had fallow plots or crambe or maize as the previous crop, the highest values of per-plant DM accumulation were found in the 2015–2016 growing season. However, when following safflower and sunflower, the soybean DM accumulations were higher in 2014–2015 and 2015–2016 growing seasons than in 2016–2017.
In the 2014–2015 growing season, the highest amounts of aboveground DM accumulated per-unit area were obtained when soybean was planted in succession to sunflower (9096 kg ha−1), safflower (8473 kg ha−1), and maize (8348 kg ha−1). However, in 2015–2016, the highest amount of per-area DM accumulation was a result of soybean cultivation in succession to crambe (9702 kg ha−1; Figure 2C). In 2016–2017, the previous crop had no effect on the per-area DM accumulation by soybean crops. Only when crambe and safflower were the previously planted crops did soybean DM accumulation vary between growing seasons. In succession to crambe, the highest soybean DM accumulations were found in the 2015–2016 and 2016–2017 growing seasons, whereas in succession to safflower the highest value was observed in 2016–2017, differing from the 2015–2016 growing season.

3.3. Aboveground Macronutrient Concentration and Uptake

The previously planted crop did not affect N, P, K, or Mg concentrations in the aboveground portion of soybean plants (Table 3). However, higher concentrations of S were observed in soybean plants cultivated following sunflower when compared with safflower. Regardless of the previous crop, the highest concentration K in soybean aboveground were observed in 2014–2015, whereas the highest concentrations of Mg and S were found in the 2016–2017 growing season. The Ca concentration in soybean aboveground was affected by the growing season, previous crop, and their interaction. The Ca concentration was higher in the soybean crop in succession to maize (15.3 g kg−1) in the 2014–2015 growing season and in succession to sunflower (9.8 g kg−1) in 2016–2017, but it did not differ as a result of previously planted crops in 2015–2016 (Figure 3A). Regardless of the previous crop, the highest Ca concentrations in soybean aboveground were found in the first growing season.
Nitrogen uptake by the soybean crop ranged from 222 to 273 kg ha−1, but it was not significantly affected by the factors studied (Table 3). The uptake of P, K, Ca, and Mg were affected by the growing season, previous crop, and their interaction. In 2014–2015, sunflower as the previously planted crop resulted in the highest uptake of P (18 kg ha−1), K (335 kg ha−1), and Mg (37 kg ha−1) by soybeans, whereas the highest uptake of Ca was as a result of maize being the previously planted crop (Figure 3B–E). Crambe as the previously planted crop caused the soybeans to have the highest P uptakes in the 2015–2016 (19 kg ha−1) and 2016–2017 (20 kg ha−1) growing seasons, whereas it also resulted in the higher uptakes of K (258 kg ha−1), Ca (73 kg ha−1), and Mg (42 kg ha−1) in 2015–2016. In the 2016–2017 growing season, the highest Ca uptake (82 kg ha−1) by the soybean crop was obtained in succession to sunflowers (Figure 3D). Regardless of the growing season, the greater uptake of S by soybean was also caused by the preceding cultivation of sunflower (Table 3). In general, the highest uptakes of K and Ca were observed in the 2014–2015 growing season, whereas the highest uptakes of P, Mg, and S occurred in 2016–2017 (Table 3; Figure 3B–E).

3.4. Yield Components and Soybean Yield

The highest final plant population of the soybean crop was obtained in the 2016–2017 growing season, with the lowest observed in 2015–2016 (Table 4). The preceding cultivation of sorghum provided on average a greater plant population of soybean than when it was grown following the plots lying fallow, or the cultivation of crambe or maize.
The number of pods per plant was affected by the growing season, the previous crop, and their interactions (Table 4). In general, the highest numbers of pods per plant were obtained in the 2015–2016 growing season, and only in this growing season did the soybeans grown after maize have a higher value of this variable than in the other treatments (Figure 4A). Only in 2015–2016 did the number of seeds per pod vary depending on the previous crop, with the highest value obtained with maize as the previously planted crop and the lowest with safflower (Figure 4B). In general, the lowest number of seeds per pod was found in the first growing season.
The soybean 1000-seed weight was affected only by the growing season, with lower weights observed in 2015–2016 (Table 4).
Furthermore, the growing season, previous crop, and their interactions significantly affected soybean seed yield (Table 4). The highest soybean seed yields were obtained in the 2015–2016 and 2016–2017 growing seasons with soybean cultivation in succession to maize (4060 kg ha−1, which was 37% higher than observed for fallow plots) and sunflower (4562 kg ha−1, which was 45% higher than observed for fallow plots), respectively (Figure 4C). In these two growing seasons, the lowest soybean yields were obtained in plots that had lain fallow during the soybean off-season.

3.5. Principal Component Analysis

Principal component analysis ordered the interrelationships between variables and treatments (previous crops), with an accumulated variance of data of 67.9% in the two principal components (PC1 and PC2; Figure 5). Although there are different recommendations regarding the percentage of total variance, between 70% and 90% being ideal [32], values above 60% may be acceptable, depending on the area of study to which the analysis is applied [33]. Therefore, we proceeded with the analysis considering 67.9% of the total variation of the data as acceptable for the present study. The clusters showed a relationship between the previous crops crambe, maize, safflower, and sunflower in the 2014–2015 growing season, with aboveground concentrations and uptake of K and Ca, in addition to the 1000-seed weight of soybean crop (Figure 5; quadrant I). In quadrant II, per-unit area DM, P, Mg, and S concentrations in the aboveground, and uptake of N, P, S, and Mg of the soybean crop were related to the crambe and fallow plots in 2015–2016 and 2016–2017, and with safflower, sorghum, and sunflower as previous crops in the 2016–2017 growing season (Figure 5). The number of seeds per pod and seed yield of soybean crop were strongly influenced by sunflower as the previously planted crop in 2015–2016, which was also interrelated with the number of pods per plant (Figure 5; quadrant III). In general, sunflower as the previous crop was related to aboveground nutrient concentrations and nutrient (N, P, K, Ca, Mg, and S) uptake, in addition to the yield components and seed yield of the soybean crop.

4. Discussion

The annual crop rotation system is an efficient strategy to meet part of the nutritional demand of the subsequent crop, due to the gradual release of nutrients during the decomposition of the biomass of the preceding crops [34,35]. For example, Mauad et al. [36] reported a C/N ratio of 14/1 in the crambe aboveground biomass, and this may have favored the mineralization of the straw remaining on the soil surface and, consequently, increased N availability for the subsequent crop [37]. Although there is some variation in the leaf concentrations of N, P, Ca, Mg, and S throughout the growing seasons and/or among the previous crops, the values found in the soybean diagnosis leaf remained within the range considered adequate by Quaggio et al. [28].
Nunes et al. [38] found that the presence of more than 10,000 kg ha−1 of sorghum residues on the soil surface reduced the growth of soybean plants in succession; notwithstanding this effect was not observed in the present study. Bossolani et al. [39] reported a higher K return for soybean in succession to sorghum. However, this was only observed in the 2016–2017 growing season during the course of this study. Furthermore, although there was a trend of a higher concentration of K in the aboveground parts of soybean in succession to sorghum, this was not reflected in higher K uptake by the soybean crop in any of the growing seasons. Although it does not produce a large amount of DM, crambe is a good K recycler [20,40], which may be the reason the soybean crop showed a higher K uptake in succession to crambe than the other fall–winter crops, especially in the last two growing seasons. In the 2014–2015 growing season, the preceding crops of safflower and sunflower provided greater K uptake by the soybean crop. Potassium is also the most accumulated nutrient in the aboveground biomass of safflower and sunflower [18,41]. It is worth noting that the K uptake by the soybean crop ranged from 217 kg ha−1 in succession to fallow to 269 kg ha−1 in succession to crambe, which is well above that reported by Quaggio et al. [28]. These high K uptakes may also have been due to the high initial K concentration in the soil (6.4 mmolc dm−3). The higher amounts of Ca taken up in the 2014–2015 growing season may have been due to the characteristic of the cultivar used [42], as well as the conditions of water availability (Figure 1), which may have favored the nutrient uptake. On the other hand, the uptake of Mg and S by the soybean crop increased throughout the growing seasons, which suggests that the crop rotation and the no-tillage system favor the cycling of these nutrients. The high amounts of per-plant and per-unit area aboveground DM accumulated by soybean crop in succession to crambe and sunflower may be associated with the C/N ratio of the straw of these crops remaining on the soil surface, which allowed rapid availability of nutrients for soybean in succession [39]. Sodré Filho et al. [43] reported a C/N ratio of 13/1 in sunflower. According to Cantarella [37], C/N ratio values between 12/1 and 25/1 favor the mineralization of the straw remaining on the soil surface and consequently increase the availability of nutrients for the following crop. Oilseed plants such as crambe and sunflower also have a relatively high S uptake [20,36,40,41]. Thus, due to their low C/N ratio, when these plants are grown as preceding crops, they may have provided greater amounts of nutrients in the soil for subsequent uptake by soybeans grown in succession, and, consequently, increasing the amount of DM accumulated in soybean plants.
Although the differences were not statistically significant, crambe and sunflower as the preceding crops allowed the highest uptake of N, P, K, Ca, Mg, and S by the subsequent soybean crop, on average during the growing seasons. For example, soybean cultivated following crambe and sunflower had uptakes of 50–51 kg N, 3–4 kg P, 42–52 kg K, 17–21 kg Ca, 6–7 kg Mg, and 2–3 kg S per ha, higher than soybean cultivated in succession to fallow. These increased amounts of N and K taken up represent approximately 20 and 40%, respectively, of the amounts of N and K required by the soybean crop for the productivity level presented in the study [28] and are greater than the amounts supplied by fertilization (Table 1). The production and maintenance of straw on the soil surface, as well as the dynamics of nutrient release, are characteristics of paramount importance for the success of NT systems [33]. Thus, studies developed with the aim of evaluating the persistence and release of nutrients from the residues of different crops corroborate the present study. Heinz et al. [40] and Mauad et al. [36] reported high levels of macronutrients in crambe aboveground biomass; however, a low persistence was also verified by the mentioned authors. In sunflower, Raphael et al. [44] reported a higher degree of humification of soil organic matter where sunflower was grown in fall–winter, i.e., there was an increase in soil cation exchange capacity.
Furthermore, crops such as crambe and sunflower stand out for their great ability to recycle nutrients in different cropping systems and are consistent with the results reported previously in the present study [20,41,45,46]. This demonstrates that such crops grown in fall–winter in the NT system in rotation with soybean can enhance the supply of nutrients in the following crop, so that recycling its residue in the soil can be one of the best alternative practices to restore the fertility of depleted soil [44,45]. Crop rotation affects most soil biochemical attributes, and thus has a good chance to affect crop yield to some degree [47,48].
The lower population of soybean plants in succession to maize was reflected in a higher number of pods per plant, especially in the 2015–2016 growing season, although it was not followed in the same way by aboveground DM production per plant. The variation in 1000-seed weight results among growing seasons may have been influenced by the genetic characteristics of each cultivar [49]. Other trials that used more than one cultivar also reported an influence of the type of soybean growth on the mentioned variables [50,51,52]. The distribution of rainfall in the different growing seasons may also have contributed to the variations in the number of pods per plant, number of seeds per pod, and 1000-seed weight. In both vegetative growth, pod formation, and seed filling, water availability is a determining factor [53,54].
With the exception of the 2014–2015 growing season, in which soybean seed yield was reduced by the lack of rain and high temperatures in the pre- and post-flowering periods (Figure 1), the lowest soybean seed yields were obtained in succession to fallow plots, and this treatment had a low relationship with the variables analyzed in the PCA (Figure 5). Pacheco et al. [55] found that fallow fields, even with high rates of nutrient release in the soil, led to low biomass production, which did not favor the accumulation of nutrients during the off-season and compromised its ability to cycle nutrients in the NT system and, consequently, release for crops planted later. Ferreira et al. [56] found no differences in the seed yield of soybean grown in succession to the fall–winter crops of maize, sunflower, millet (Pennisetum glaucum (L.) R. Brown), wheat (Triticum aestivum L.), common bean (Phaseolus vulgaris L.), and fallow fields in the central-west region of Brazil; however, soil cultivation tended to provide greater seed yield of subsequent soybean crop than areas that had lain fallow in the fall–winter period. Calonego and Rosolem [57] and Calonego et al. [58] found that root growth in depth and seed yield of soybean were increased under rotation with triticale (X Triticosecale Wittmack), millet, and sunn hemp (Crotalaria juncea L.) due to the presence of biopores, increase in macroporosity, and a decrease in soil penetration resistance.
The highest soybean seed yields obtained in succession with maize (2015–2016) and sunflower (2016–2017) as previously planted crops point to the importance of choosing which plant species to compose the crop rotation system whose effect is expressive and positive on the seed yield of the subsequent soybean crop [59]. Cordeiro et al. [48] found that, compared to sunflower and pigeon pea (Cajanus cajan (L.) Millsp), fall–winter maize stimulated more amylase, cellulase, urease, and protease enzyme activities in the rhizospheric soil, as well as the activity of amylase, urease, and protease enzymes in soil non-rhizospheric soil, which can be indicators of improved soil health. They also found that fall–winter maize and sunflower increased total carbohydrates in the soil. Other studies evaluating the soybean crop in different cropping systems have been carried out. For instance, Marcelo et al. [22] evaluated the seed yield of soybean in succession to maize, sunflower, oilseed radish (Raphanus sativus L.), millet, pigeon pea, sorghum, and sunn hemp in southeastern Brazil. These authors obtained the highest seed yields when the soybean was cultivated in succession to radish and sunn hemp, and they explained that this happened because oilseed radish and sunn hemp increased topsoil (0–0.05 m) P levels and soil organic matter content up to 0.30 m depth. Oilseed radish accumulated high P levels and showed a fast decomposition of plant residues [60]. In a Typic Haplustox of the Brazilian Cerrado, the cultivation and residue decomposition of sunn hemp increased soil P levels by about 7 mg dm−3 in the 0–0.15-m layer [61]. Crops with an abundant and aggressive root system, which allocate a larger fraction of the photosynthesized carbon to the roots, may be more efficient in increasing the soil organic matter [44,57,62]. Marcelo et al. [22] also found that summer soybean yields were always lower when sown in succession to harvested fall–winter crops (maize, sunflower, and sorghum), which indicates that nutrient export results in lower nutrient availability for the following crop. In our study, all fall–winter crops had their seeds harvested.
In eastern South Dakota, USA, Riedell et al. [15] evaluated soybean seed yield in cropping systems (soybean/maize during two years of rotation; soybean/wheat/maize for three years; soybean–barley (Hordeum vulgare L.)/pea (Pisum sativum L.)–maize for three years; soybean–barley/pea–alfalfa (Medicago sativa L.)–maize for five years). According to the authors, the soybean–barley/pea–alfalfa–maize cropping system for five years resulted in the highest soybean seed yield. For these authors, the decrease in soil bulk density and the increase in soil NO3–N under extended rotations that contain a diversity of crops played important roles in affecting mineral nutrition levels and increasing the seed yield of subsequent soybean crops. Thus, differences in root growth, and their consequent effects on physical, chemical, and biological attributes of the soil, as well as variations in the ability to uptake, remove, and cycle nutrients and in the speed of decomposition of residues left in the area are characteristics that interfere with the effect that each fall–winter crop plays on the subsequent soybean crop [6,9,13,15,18,20,22,35,38,44,47,55,56,57,58,62]. On the other hand, at Eastern Cape, South Africa, Gura and Mnkeni [63] found that the soil biological properties were more responsive to residue management than crop rotations in the short-term. In addition, climate and soil type (e.g., texture, depth, drainage, content of nutrients, etc.) can also greatly interfere with the effect of crop rotation on the yield of subsequent crops [6,11,14,49,50].
Weisberger et al. [64] found diversifying from simple rotations reduced weed density under zero-tillage conditions by 65%, and did so regardless of environmental context and auxiliary herbicide use; however, diversification that increased the variance around crop planting dates was more effective in suppressing weeds than increasing crop species richness alone. In more diverse rotations, weed seed germination and seedling mortality, targeted herbicides, and altered crop sowing density or row spacing configurations may play a stronger role in suppressing weeds. In our study, weeds were controlled in the plots cultivated in a double-crop system (i.e., including a fall–winter crop) but not in the plots that had lain fallow. In the spring–summer soybean crop, weed control was performed uniformly in all plots. In the growing seasons with better weather conditions for soybean development (as in 2015–2016 and 2016–2017), the highest seed yields occurred. However, when plots were allowed to lie fallow during soybean off-seasons, the seed yields were lower. Conversely, the use of a double-crop system, by adding a fall–winter cash crop to the system, allowed soybeans to reach high levels of seed yield, with increases of up to 45%. We also need to consider that the previous fall–winter crops were fertilized and had their seeds harvested, which did not happen in the plots that were allowed to lie fallow. Therefore, although the results are not consistent, they suggest that choosing an appropriate cropping sequence in the soybean crop rotation, such as maize, safflower, or sunflower, can be crucial in obtaining high soybean yields in the following season.

5. Conclusions

This three-year field study provides evidence that letting fields lie fallow (spontaneous weeds) during the off-season reduced the DM accumulation and nutrient uptake of the soybean crop in succession, especially in years with unfavorable weather conditions. Preceding cultivation of fall–winter crambe or sunflower favored the uptake of P, K, Ca, Mg, and S by the subsequent soybean crop. Furthermore, the cultivation of maize or sunflower in the fall–winter period allowed the highest soybean seed yields, i.e., increased them respectively by 37% and 45% compared to the fallow, especially after two or three growing seasons. However, the cultivation of sorghum, safflower, and crambe as fall–winter crops also favored the seed yield of subsequent soybean crops (i.e., increased from 12 to 18% on the average of three growing seasons) and may be options for crop rotation. Further studies, also evaluating soil quality and for a longer period of time, are necessary to define the best fall–winter crop to be rotated with the soybean crop in tropical regions of Brazil.

Author Contributions

Conceptualization, R.P.S.; methodology, F.V.C.G. and A.L.G.J.; software, F.V.C.G. and W.S.S.; formal analysis, R.P.S., F.V.C.G., A.P.G. and W.S.S.; investigation, F.V.C.G. and A.L.G.J.; data curation, R.P.S.; writing—original draft preparation, F.V.C.G., A.P.G. and W.S.S.; writing—review and editing, R.P.S. and J.C.C.; supervision, R.P.S.; project administration, R.P.S.; funding acquisition, R.P.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Acknowledgments

The authors are thankful to the National Council for Scientific and Technological Development (CNPq) for providing an award for excellence in research to the first author (Porc. 304736/2018-0) and a fellowship to the third author (Proc. 141190/2021-3).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. FAO—Food and Agriculture Organization of the United Nations. Food and Agriculture Data. Production-Crops; FAO: Rome, Italy, 2022; Available online: https://www.fao.org/faostat/en/#data/QCL (accessed on 24 September 2022).
  2. Chen, K.I.; Erh, M.H.; Su, N.W.; Liu, W.H.; Chou, C.C.; Cheng, K.C. Soyfoods and soybean products: From traditional use to modern applications. App. Microb. Biotec. 2012, 96, 9–22. [Google Scholar] [CrossRef] [PubMed]
  3. CONAB—Companhia Nacional de Abastecimento. Portal de Informações Agropecuárias, Safras 2020/21. Conab. 2022. Available online: https://portaldeinformacoes.conab.gov.br/safra-serie-historica-graos.html (accessed on 14 September 2022).
  4. Merten, G.H.; Araújo, A.G.; Biscaia, R.C.M.; Barbosa, G.M.C.; Conteb, O. No-till surface runoff and soil losses in southern Brazil. Soil Tillage Res. 2015, 152, 85–93. [Google Scholar] [CrossRef]
  5. Rosa, C.B.C.J.; Marchetti, M.E.; Serra, A.P.; Rosa, M.S.M.; Ensinas, S.C.; Conrad, V.A.; Altomar, P.H.; Potrich, D.C.; Martinez, M.A. Short-term effects of lime management in soybean no-tillage system implementation in Brazilian savannah. Aus. J. Crop Sci. 2015, 9, 232–241. [Google Scholar]
  6. Freitas, M.E.; Souza, L.C.F.; Salton, J.C.; Serra, A.P.; Mauad, M.; Cortez, J.W.; Marchetti, M.E. Crop rotation affects soybean performance in no-tillage system under optimal and dry cropping seasons. Aus. J. Crop Sci. 2016, 10, 353–361. [Google Scholar] [CrossRef]
  7. Fuentes-Llanillo, R.; Telles, T.S.; Soares Junior, D.; Melo, T.R.; Friedrich, T.; Kassam, A. Expansion of no-tillage practice in conservation agriculture in Brazil. Soil Tillage Res. 2021, 208, 104877. [Google Scholar] [CrossRef]
  8. Segatelli, C.R.; Câmara, G.M.S.; Aguila, L.S.H.D.; Aguila, J.S.D.; Francisco, E.A.B.; Piedade, S.M.S. Soybean yield under no-tillage system with an early Eleusine coracana fertilization. Rev. Caatinga 2022, 35, 308–319. [Google Scholar] [CrossRef]
  9. Calonego, J.C.; Rosolem, C.A. Estabilidade de agregados do solo após manejo com rotação de culturas e escarificação. Rev. Bras. Ciênc. Solo 2008, 32, 1399–1407. [Google Scholar] [CrossRef] [Green Version]
  10. Galdos, M.V.; Pires, L.F.; Cooper, H.V.; Calonego, J.C.; Rosolem, C.A.; Mooney, S.J. Assessing the long-term effects of zero-tillage on the macroporosity of Brazilian soils using X-ray Computed Tomography. Geoderma 2019, 337, 1126–1135. [Google Scholar] [CrossRef]
  11. Aranda, V.; Ayora-Cañada, M.J.; Domínguez-Vidal, A.; Martín-García, J.M.; Calero, J.; Delgado, R.; Verdejo, T.; González-Vila, F.J. Effect of soil type and management (organic vs. conventional) on soil organic matter quality in olive groves in a semi-arid environment in Sierra Mágina Natural Park (Spain). Geoderma 2011, 164, 54–63. [Google Scholar] [CrossRef]
  12. Cooper, H.V.; Sjögersten, S.; Lark, R.M.; Girkin, N.T.; Vane, C.H.; Calonego, J.C.; Rosolem, C.A.; Mooney, S.J. Long-term zero-tillage enhances the protection of soil carbon in tropical agriculture. Eur. J. Soil Sci. 2021, 72, 2477–2492. [Google Scholar] [CrossRef]
  13. Crusciol, C.A.C.; Nascente, A.S.; Borghi, E.; Soratto, R.P.; Martins, P.O. Improving soil fertility and crop yield in a tropical region with palisadegrass cover crops. Agron. J. 2015, 107, 2271–2280. [Google Scholar] [CrossRef]
  14. Porter, P.M.; Lauer, J.G.; Lueschen, W.E.; Ford, J.H.; Hoverstad, T.R.; Oplinger, E.S.; Crookston, R.K. Environment affects the corn and soybean rotation effect. Agron. J. 1997, 89, 441–448. [Google Scholar] [CrossRef] [Green Version]
  15. Riedell, W.R.; Osborne, S.L.; Pikul, J.L., Jr. Soil attributes, soybean mineral nutrition, and yield in diverse crop rotations under no-till conditions. Agron. J. 2013, 105, 1231–1236. [Google Scholar] [CrossRef]
  16. Sindelar, A.J.; Schmer, M.R.; Jin, V.L.; Wienhold, B.J.; Varvel, G.E. Long-term corn and soybean response to crop rotation and tillage. Agron. J. 2015, 107, 2241–2252. [Google Scholar] [CrossRef] [Green Version]
  17. Fan, R.; Zhang, X.; Liang, A.; Shi, X.; Chen, X.; Bao, K.; Yang, X.; Jia, S. Tillage and rotation effects on crop yield and profitability on a Black soil in northeast China. Can. J. Soil Sci. 2012, 92, 463–470. [Google Scholar] [CrossRef]
  18. Guidorizzi, F.V.C.; Soratto, R.P.; Silva, M.M.; Pinto, L.O.G.; Fernandes, A.M.; Souza, E.F.C. Biomass and nutrient accumulation and partitioning of fall-winter safflower in a double-cropping system of southeastern Brazil. Agron. J. 2021, 113, 451–463. [Google Scholar] [CrossRef]
  19. Oliveira, C.M.; Frizzas, M.R. Eight decades of Dalbulus maidis (DeLong & Wolcott) (Hemiptera, Cicadellidae) in Brazil: What we know and what we need to know. Neotrop. Entomol. 2022, 51, 1–17. [Google Scholar] [CrossRef]
  20. Soratto, R.P.; Souza-Schlick, G.D.; Fernandes, A.M.; Souza, E.F.C. Effect of fertilization at sowing on nutrition and yield of crambe in second season. Rev. Bras. Ciênc. Solo 2013, 37, 658–666. [Google Scholar] [CrossRef]
  21. Silva, I.R.; Mendonça, E.S. Matéria orgânica do solo. In Fertilidade do Solo, 1st ed.; Novais, F.R., Alvarez, V.V.H., Barros, N.F., Fontes, R.L.F., Cantarutti, R.B., Neves, J.C.L., Eds.; Sociedade Brasileira de Ciência do Solo: Viçosa, Brazil, 2007; pp. 275–356. [Google Scholar]
  22. Marcelo, V.A.; Corá, J.E.; Fernandes, C.; Martins, M.R.; Jorge, R.F. Crop sequences in no-tillage system: Effects on soil fertility and soybean, maize and rice yield. Rev. Bras. Ciênc. Solo 2009, 33, 417–428. [Google Scholar] [CrossRef] [Green Version]
  23. FAO—Food and Agriculture Organization of the United Nations. World Reference Base for Soil Resources 2014: International Soil Classification System for Naming Soils and Creating Legends for Soil Maps; FAO: Rome, Italy, 2015; Available online: http://www.fao.org/3/i3794en/I3794en.pdf (accessed on 24 September 2022).
  24. van Raij, B.; Andrade, J.C.; Cantarella, H.; Quaggio, J.A. Análise Química Para Avaliação da Fertilidade de Solos Tropicais; Instituto Agronômico: Campinas, Brazil, 2001. [Google Scholar]
  25. EMBRAPA—Empresa Brasileira de Pesquisa Agropecuária. Manual de métodos de análise de solo; Embrapa-CNPS: Rio de Janeiro, Brasil, 1997; Available online: https://www.embrapa.br/en/busca-de-publicacoes/-/publicacao/1085209/manual-de-metodos-de-analise-de-solo (accessed on 24 September 2022).
  26. Fehr, W.R.; Caviness, C.E.; Burmood, D.T.; Pennington, J.S. Stage of development descriptions ofsoybeans, Glycine max (L.) Merr. Crop Sci. 1971, 11, 929–931. [Google Scholar] [CrossRef]
  27. Munger, P.; Bleiholder, H.; Hack, H.; Hess, M.; Stauss, R.; van den Boom, T.; Weber, E. Phenological growth stages of the soybean plant {Glycine max L. Merr.): Codification and description according to the BBCH scale. J. Agron. Crop Sci. 1997, 179, 209–217. [Google Scholar] [CrossRef]
  28. Quaggio, J.A.; Cantarella, H.; Rosolem, C.A.; Crusciol, S. Boletim 100: Recomendação de Adubação e Calagem Para o Estado de São Paulo; Cantarella, H., Quaggio, J.A., Mattos, D., Jr., Boaretto, R.M., van Raij, B., Eds.; Instituto Agronômico: Campinas, Brazil, 2022; pp. 209–212. [Google Scholar]
  29. Malavolta, E.; Vitti, G.C.; Oliveira, S.A. Avaliação do Estado Nutricional das Plantas: Princípios e Aplicações, 2nd ed.; Associação Brasileira para Pesquisa da Potassa e do Fosfato: Piracicaba, Brazil, 1997. [Google Scholar]
  30. Ferreira, D.F. Sisvar: A Guide for its Bootstrap procedures in multiple comparisons. Ciênc. Agrotecnol. 2014, 38, 109–112. [Google Scholar] [CrossRef]
  31. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2022; Available online: https://www.R-project.org/ (accessed on 11 September 2021).
  32. Zwick, W.R.; Velicer, W.F. Factors influencing four rules for determining the number of components to retain. Multivariate Behav. Res. 1982, 17, 253–269. [Google Scholar] [CrossRef]
  33. Hair, J.F. Multivariate Data Analysis: An Overview. In International Encyclopedia of Statistical Science; Lovric, M., Ed.; Springer: Berlin, Heidelberg, 2011. [Google Scholar] [CrossRef]
  34. Teixeira, M.B.; Loss, A.; Pereira, M.G.; Pimentel, C. Decomposição e ciclagem de nutrientes dos resíduos de quatro plantas de cobertura do solo. Idesia 2012, 30, 55–64. [Google Scholar] [CrossRef] [Green Version]
  35. Miguel, A.S.D.C.S.; Pacheco, L.P.; Carvalho, I.C.; Souza, E.D.; Feitosa, P.B.; Petter, F.A. Phytomass and nutrient release in soybean cultivation systems under no-tillage. Pesqu. Agropec. Bras. 2018, 53, 1119–1131. [Google Scholar] [CrossRef]
  36. Mauad, M.; Vitoriono, A.C.T.; Souza, L.C.F.; Heinz, R.; Garbiate, M.V. Straw persistence and nutrient release from crambe abyssinica acoording to the time of management. Rev. Ciênc. Agrár. 2013, 56, 53–60. [Google Scholar] [CrossRef]
  37. Cantarella, H. Nitrogênio. Fertilidade do Solo, 1st ed.; Novais, F.R., Alvarez, V.V.H., Barros, N.F., Fontes, R.L.F., Cantarutti, R.B., Neves, J.C.L., Eds.; Sociedade Brasileira de Ciência do Solo: Viçosa, Brazil, 2007; pp. 375–470. [Google Scholar]
  38. Nunes, J.C.S.; Araujo, E.F.; Souza, C.M.; Bertini, L.A.; Ferreira, F.A. Efeito da palhada de sorgo localizada na superfície do solo em características de plantas de soja e milho. Rev. Ceres 2003, 50, 115–128. [Google Scholar]
  39. Bossolani, J.W.; Lazarini, E.; Souza, L.G.M.; Parente, T.L.; Caioni, S.; Biazi, N.Q. Potassium doses in previous crops and effect on soybean in succession. Rev. Bras. Eng. Agríc. Amb. 2018, 22, 90–94. [Google Scholar] [CrossRef] [Green Version]
  40. Heinz, R.; Garbiate, M.V.; Neto, A.L.V.; Mota, L.H.S.; Correia, A.M.P.; Vitorino, A.C.T. Decomposição e liberação de nutrientes de resíduos culturais de crambe e nabo forrageiro. Ciênc. Rural 2011, 41, 1549–1555. [Google Scholar] [CrossRef]
  41. Zobiole, L.H.S.; Castro, C.; Oliveira, F.A.; Oliveira Junior, A. Marcha de absorção de macronutrientes na cultura do girassol. R. Bras. Ci. Solo 2010, 34, 425–433. [Google Scholar] [CrossRef] [Green Version]
  42. Bortolon, L.; Bortolon, E.S.O.; Camargo, F.P.C.; Seraglio, N.A.; Lima, A.O.; Rocha, P.H.F.; Souza, J.P.; Sousa, W.C.; Tomazzi, M.; Lago, B.C.; et al. Yield and nutrient uptake of soybean cultivars under intensive cropping systems. J. Agr. Sci. 2018, 10, 344–357. [Google Scholar] [CrossRef]
  43. Sodré Filho, J.; Cardoso, A.N.; Carmona, R.; Carvalho, A.M. Fitomassa de cobertura do solo de culturas de sucessão ao milho na região do cerrado. Pesqu. Agropec. Bras. 2004, 39, 327–334. [Google Scholar] [CrossRef] [Green Version]
  44. Raphael, J.P.A.; Calonego, J.C.; Milori, D.M.B.P.; Rosolen, C.A. Soil organic matter in crop rotations under no-till. Soil Tillage Res. 2016, 155, 45–53. [Google Scholar] [CrossRef] [Green Version]
  45. Babu, S.; Rana, D.S.; Yadav, G.S.; Singh, R.; Yadav, S.K. A review on recycling of sunflower residue for sustaining soil health. Inter. J. Agron. 2014, 2014, 601049. [Google Scholar] [CrossRef]
  46. Samarappuli, D.; Zanetti, F.; Berzuini, S.; Berti, M.T. Crambe (Crambe abyssinica Hochst): A non-food oilseed crop with great potential: A review. Agronomy 2020, 10, 1380. [Google Scholar] [CrossRef]
  47. Campbell, C.A.; Biederbeck, V.O.; Wen, G.; Zentner, R.P.; Schoenau, J.; Hahn, D. Seasonal trends in selected soil biochemical attributes: Effects of crop rotation in the semiarid prairie. Can. J. Soil Sci. 1999, 79, 73–84. [Google Scholar] [CrossRef]
  48. Cordeiro, M.A.S.; Corá, J.E.; Nahas, E. Atributos bioquímicos e químicos do solo rizosférico e não rizosférico de culturas em rotação no sistema de semeadura direta. Rev. Bras. Cienc. Solo 2012, 36, 1794–1803. [Google Scholar] [CrossRef] [Green Version]
  49. Gross, M.R.; Van, R.A.; Woodley, A.L.; Jordan, D. Winter crop effect on soybean production in the Southeast United States. Agron. J. 2022, 114, 662–677. [Google Scholar] [CrossRef]
  50. Anda, J.E.; Soós, G.; Menyhárt, L.; Kucserka, T.; Simon, B. Yield features of two soybean varieties under different water supplies and field conditions. Field Crops Res. 2020, 245, 107673. [Google Scholar] [CrossRef]
  51. Cover, J.E.; Aguiar, C.D.; Silva, A.V.; Silva, C.M.; Mielezrski, F. Productive potential and seed quality of soybean genotypes with different maturity groups. Aus. J. Crop Sci. 2019, 13, 1155–1161. [Google Scholar] [CrossRef]
  52. Souza, R.; Teixeira, I.; Reis, E.; Silva, A. Soybean morphophysiology and yield response to seeding systems and plant populations. Chil. J. Agric. Res. 2016, 76, 3–8. [Google Scholar] [CrossRef]
  53. Leolato, L.S.; Sangoi, L.; Souza, C.A.; Kuneski, H.F.; Scherer, R.L.; Oliveira, V.L.; Martins Junior, M.C.; Kandler, R. Soybean tolerance to defoliation at the vegetative and reproductive stages as a function of water restriction. Acta Scient. Agron. 2022, 44, e55639. [Google Scholar] [CrossRef]
  54. Silva, A.J.; Magalhães Filho, J.R.; Sales, C.R.G.; Pires, R.C.M.; Machado, E.C. Source-sink relationships in two soybean cultivars with indeterminate growth under water deficit. Bragantia 2018, 77, 23–35. [Google Scholar] [CrossRef] [Green Version]
  55. Pacheco, L.P.; Barbosa, J.M.; Leandro, W.M.; Machado, P.L.O.A.; Assis, R.L.; Madari, B.E.; Petter, F.A. Ciclagem de nutrientes por plantas de cobertura e produtividade de soja e arroz em plantio direto. Pesqu. Agropec. Bras. 2013, 48, 1228–1236. [Google Scholar] [CrossRef] [Green Version]
  56. Ferreira, W.S.; Braz, A.J.B.P.; Assis, R.L.; Costa, K.A.P.; Silva, A.G.; Torres, J.L.R. Cultivo do milho e da soja em sucessão as culturas de safrinha em Rio Verde-GO. Energ. Agric. 2016, 31, 291–297. [Google Scholar] [CrossRef] [Green Version]
  57. Calonego, J.C.; Rosolem, C.A. Soybean root growth and yield in rotation with cover crops under chiseling and no-till. Eur. J. Agron. 2010, 33, 242–249. [Google Scholar] [CrossRef]
  58. Calonego, J.C.; Raphael, J.P.A.; Rigon, J.P.G.; Oliveira Neto, L.; Rosolem, C.A. Soil compaction management and soybean yields with cover crops under no-till and occasional chiseling. Eur. J. Agron. 2017, 85, 31–37. [Google Scholar] [CrossRef] [Green Version]
  59. Zegada-Lizarazu, W.; Monti, A. Energy crops in rotation. A review. Biomass Bioenergy 2011, 35, 12–25. [Google Scholar] [CrossRef]
  60. Crusciol, C.A.C.; Cottica, R.L.; Lima, E.V.; Andreotti, M.; Moro, E.; Marcon, E. Persistência de palhada e liberação de nutrientes do nabo forrageiro no plantio direto. Pesqu. Agropec. Bras. 2005, 40, 161–168. [Google Scholar] [CrossRef] [Green Version]
  61. Collier, L.S.; Castro, D.V.; Dias Neto, J.J.; Brito, D.R.; Ribeiro, P.A.A. Manejo da adubação nitrogenada para milho sob palhada de leguminosas em plantio direto em Gurupi, TO. Cienc. Rural 2006, 36, 1100–1105. [Google Scholar] [CrossRef] [Green Version]
  62. Carneiro, C.E.A.; Melém Júnior, N.J.; Azevedo, M.C.B.; Andrade, E.A.; Koguishi, M.C.; Diehl, R.C.; Ricce, W.; Passarin, A.L.; Vaz, R.H.M.; Stelmachuk, T.L.L.; et al. Efeitos dos sistemas de manejo sobre o carbono orgânico total e carbono residual de um Latossolo Vermelho eutroférrico. Semina 2009, 30, 5–10. [Google Scholar] [CrossRef]
  63. Gura, I.; Mnkeni, P.N.S. Crop rotation and residue management effects under no till on the soil quality of a Haplic Cambisol in Alice, Eastern Cape, South Africa. Geoderma 2019, 337, 927–934. [Google Scholar] [CrossRef]
  64. Weisberger, D.; Nichols, V.; Liebman, M. Does diversifying crop rotations suppress weeds? A meta-analysis. PLoS ONE 2019, 14, e0219847. [Google Scholar] [CrossRef] [PubMed]
Agronomy 12 02974 g001 550
Figure 1. Daily rainfall (green bar) and maximum (red line) and minimum (blue line) temperatures in the experimental area at Botucatu, São Paulo State, Brazil, during the period from April to March in the 2014–2015 (A), 2015–2016 (B), and 2016–2017 (C) periods, as well as 50-year averaged monthly rainfall (grey bar) and maximum (black line) and minimum (grey line) temperatures (D). Dashed lines indicate dates of sowing of the previous crops and sowing, flowering, and maturation of soybean crop in each growing season.
Figure 1. Daily rainfall (green bar) and maximum (red line) and minimum (blue line) temperatures in the experimental area at Botucatu, São Paulo State, Brazil, during the period from April to March in the 2014–2015 (A), 2015–2016 (B), and 2016–2017 (C) periods, as well as 50-year averaged monthly rainfall (grey bar) and maximum (black line) and minimum (grey line) temperatures (D). Dashed lines indicate dates of sowing of the previous crops and sowing, flowering, and maturation of soybean crop in each growing season.
Agronomy 12 02974 g001
Agronomy 12 02974 g002 550
Figure 2. Growing season × previous crop interaction for leaf K concentration (A) and per-plant (B) and per-unit area (C) aboveground dry matter of spring–summer soybean crop. Error bars represent standard errors of the mean. Lowercase letters compare previous crops within each growing season, whereas uppercase letters compare growing seasons within each previous crop at p < 0.05 according to Tukey’s test.
Figure 2. Growing season × previous crop interaction for leaf K concentration (A) and per-plant (B) and per-unit area (C) aboveground dry matter of spring–summer soybean crop. Error bars represent standard errors of the mean. Lowercase letters compare previous crops within each growing season, whereas uppercase letters compare growing seasons within each previous crop at p < 0.05 according to Tukey’s test.
Agronomy 12 02974 g002
Agronomy 12 02974 g003 550
Figure 3. Growing season × previous crop interaction for Ca concentration in the aboveground (A) and P (B), K (C), Ca (D), and Mg (E) uptake of spring–summer soybean crop. Error bars represent standard errors of the mean. Lowercase letters compare previous crops within each growing season, whereas uppercase letters compare growing seasons within each previous crop at p < 0.05 according to Tukey’s test.
Figure 3. Growing season × previous crop interaction for Ca concentration in the aboveground (A) and P (B), K (C), Ca (D), and Mg (E) uptake of spring–summer soybean crop. Error bars represent standard errors of the mean. Lowercase letters compare previous crops within each growing season, whereas uppercase letters compare growing seasons within each previous crop at p < 0.05 according to Tukey’s test.
Agronomy 12 02974 g003
Agronomy 12 02974 g004 550
Figure 4. Growing season × previous crop interaction for number of pods per plant (A), number of seeds per pod (B), and seed yield (C) of spring–summer soybean crop. Error bars represent standard errors of the mean. Lowercase letters compare previous crops within each growing season, whereas uppercase letters compare growing seasons within each previous crop at p < 0.05 according to Tukey’s test.
Figure 4. Growing season × previous crop interaction for number of pods per plant (A), number of seeds per pod (B), and seed yield (C) of spring–summer soybean crop. Error bars represent standard errors of the mean. Lowercase letters compare previous crops within each growing season, whereas uppercase letters compare growing seasons within each previous crop at p < 0.05 according to Tukey’s test.
Agronomy 12 02974 g004
Agronomy 12 02974 g005 550
Figure 5. Principal component analysis (PCA) for previous crop treatments, including all growing seasons. Six previous crops in the three growing seasons (numbers from 1 to 18 in the circles) and vectors (yellow arrows) represent trait factor loading coordinates for PC 1 and PC 2. Traits measured in soybean crop were concentrations in aboveground (AG_N, AG_P, AG_K, AG_Ca, AG_Mg e AG_S), macronutrient uptake (N_uptake, P_uptake, K_uptake, Ca_uptake, Mg_uptake e S_uptake), and per-unit area dry matter (ADM_area), number of pods per plant (NPP), number of seeds per pod (NSP), 1000-seed weight (1000SW), and seed yield (Yield). The red lines were drawn to facilitate visualization of groups of treatments and their interrelationships with the variables.
Figure 5. Principal component analysis (PCA) for previous crop treatments, including all growing seasons. Six previous crops in the three growing seasons (numbers from 1 to 18 in the circles) and vectors (yellow arrows) represent trait factor loading coordinates for PC 1 and PC 2. Traits measured in soybean crop were concentrations in aboveground (AG_N, AG_P, AG_K, AG_Ca, AG_Mg e AG_S), macronutrient uptake (N_uptake, P_uptake, K_uptake, Ca_uptake, Mg_uptake e S_uptake), and per-unit area dry matter (ADM_area), number of pods per plant (NPP), number of seeds per pod (NSP), 1000-seed weight (1000SW), and seed yield (Yield). The red lines were drawn to facilitate visualization of groups of treatments and their interrelationships with the variables.
Agronomy 12 02974 g005
Table
Table 1. Dates of agrotechnical practices, fertilizations, and soybean cultivars in each growing season.
Table 1. Dates of agrotechnical practices, fertilizations, and soybean cultivars in each growing season.
PracticeGrowing Season
2014–20152015–20162016–2017
Previous crop sowing04 Apr. 201417 Apr. 201513 Apr. 2016
Previous crop fertilization8 kg N, 12.4 kg P, and 13.4 kg K per ha
Spontaneous weeds termination5 Nov. 201421 Oct. 201510 Nov. 2016
Soybean cultivarBMX Potência RRTMG2158IPROM5917IPRO
Soybean sowing17 Nov. 201427 Oct. 201525 Nov. 2016
Soybean fertilization4 kg N, 17.8 kg P, and 33.4 kg K per ha
Table
Table 2. Leaf nutrient concentrations and aboveground dry matter of spring–summer soybean crop following different fall–winter crops in three growing season, and analyses of variance.
Table 2. Leaf nutrient concentrations and aboveground dry matter of spring–summer soybean crop following different fall–winter crops in three growing season, and analyses of variance.
TreatmentNutrient Concentration in Leaves (g kg−1)Aboveground Dry Matter
NPKCaMgS(g plant−1)(kg ha−1)
Growing season
2014–201548.6a3.0a23.812.8b3.5ab2.5ab29.17933
2015–201648.7a2.8b26.114.5a3.7a2.3b36.07866
2016–201749.2a3.1a21.411.3c3.4b2.6a25.28441
Previous crop
Fallow48.6ab2.8a22.812.6a3.4a2.4abc28.57369
Crambe50.5a2.9a23.412.9a3.7a2.4abc35.38852
Maize46.6b3.1a23.912.6a3.7a2.8a32.87728
Safflower49.6ab3.0a24.512.3a3.4a2.1c29.68357
Sorghum48.2ab3.0a24.613.5a3.7a2.6ab24.87591
Sunflower49.2ab2.9a23.313.2a3.4a2.4abc29.68582
Source of variationP > F
Growing season (GS)0.758<0.001<0.001<0.0010.0210.028<0.0010.033
Previous crop (PC)0.0490.0910.1590.1070.089<0.001<0.001<0.001
GS × PC0.5750.9840.0210.5600.0540.103<0.0010.002
Values followed by the same letter in the column, within each factor (growing season and previous crop), are not significantly different at p < 0.05 according to the Tukey’s test.
Table
Table 3. Aboveground nutrient concentrations and uptake of spring–summer soybean crop following different fall–winter crops in three growing season, and analyses of variance.
Table 3. Aboveground nutrient concentrations and uptake of spring–summer soybean crop following different fall–winter crops in three growing season, and analyses of variance.
TreatmentNutrient Concentration (g kg−1)Nutrient Uptake (kg ha−1)
NPKCaMgSNPKCaMgS
Growing season
2014–201530.0a1.9a38.1a13.74.2b1.4b241a153011093311b
2015–201630.0a2.0a25.8c7.64.1b1.5b239a16203593212b
2016–201729.6a2.0a27.6b8.14.7a2.1a250a17232684018a
Previous crop
Fallow30.2a1.9a29.8a9.54.3a1.8ab222a14217693213ab
Crambe30.6a2.0a30.8a10.04.4a1.7ab273a18269863915ab
Maize30.8a2.0a29.8a10.44.2a1.8ab238a15232823214ab
Safflower27.5a2.0a31.0a9.64.4a1.4b230a16260803712b
Sorghum29.7a2.0a31.5a8.74.3a1.6ab224a15238653312b
Sunflower31.7a2.0a30.0a10.74.4a1.8a272a17259903816a
Source of variation P > F
Growing season (GS)0.7860.084<0.001<0.001<0.001<0.0010.6690.025<0.001<0.001<0.001<0.001
Previous crop (PC)0.2170.2730.365<0.0010.8030.0360.012<0.001<0.001<0.0010.0010.014
GS × PC0.2180.4100.2250.0190.3260.4520.2270.0440.0090.0340.0360.194
Values followed by the same letter in the column, within each factor (growing season and previous crop), are not significantly different at p < 0.05 according to the Tukey’s test.
Table
Table 4. Final plant population, number of pods per plant, number of seeds per pod, 1000-seed weight, and seed yield of spring–summer soybean crop following different fall–winter crops in three growing season, and analyses of variance.
Table 4. Final plant population, number of pods per plant, number of seeds per pod, 1000-seed weight, and seed yield of spring–summer soybean crop following different fall–winter crops in three growing season, and analyses of variance.
TreatmentPlant Population (Thousand Plants ha−1)No. of Pods per Plant No. of Seeds per Pod1000-Seed Weight (g)Seed Yield (kg ha−1)
Growing season
2014–2015273.8b36.01.4169.1a2285
2015–2016226.5c59.61.8146.3b3350
2016–2017335.9a35.11.9170.1a3792
Previous crop
Fallow263.9bc41.41.6161.1a2731
Crambe261.1bc45.51.7162.2a3063
Maize254.4c52.01.7163.5a3209
Safflower287.5abc43.01.7165.4a3367
Sorghum310.2a37.11.7160.3a3082
Sunflower295.4ab42.31.7158.4a3403
Source of variationP > F
Growing season (GS)<0.001<0.001<0.001<0.001<0.001
Previous crop (PC)<0.0010.0010.4340.3890.004
GS × PC0.082<0.0010.0370.1290.002
Values followed by the same letter in the column, within each factor (growing season and previous crop), are not significantly different at p < 0.05 according to the Tukey’s test.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Soratto, R.P.; Guidorizzi, F.V.C.; Sousa, W.S.; Gilabel, A.P.; Job, A.L.G.; Calonego, J.C. Effects of Previous Fall–Winter Crop on Spring–Summer Soybean Nutrition and Seed Yield under No-Till System. Agronomy 2022, 12, 2974. https://doi.org/10.3390/agronomy12122974

AMA Style

Soratto RP, Guidorizzi FVC, Sousa WS, Gilabel AP, Job ALG, Calonego JC. Effects of Previous Fall–Winter Crop on Spring–Summer Soybean Nutrition and Seed Yield under No-Till System. Agronomy. 2022; 12(12):2974. https://doi.org/10.3390/agronomy12122974

Chicago/Turabian Style

Soratto, Rogério P., Fernando V. C. Guidorizzi, Westefann S. Sousa, Amanda P. Gilabel, André L. G. Job, and Juliano C. Calonego. 2022. "Effects of Previous Fall–Winter Crop on Spring–Summer Soybean Nutrition and Seed Yield under No-Till System" Agronomy 12, no. 12: 2974. https://doi.org/10.3390/agronomy12122974

APA Style

Soratto, R. P., Guidorizzi, F. V. C., Sousa, W. S., Gilabel, A. P., Job, A. L. G., & Calonego, J. C. (2022). Effects of Previous Fall–Winter Crop on Spring–Summer Soybean Nutrition and Seed Yield under No-Till System. Agronomy, 12(12), 2974. https://doi.org/10.3390/agronomy12122974

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop