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Article

Economic Results of Forage Species Choice in Crop–Livestock Integrated Systems

by
Vanessa Nunes Leal
1,
Darliane de Castro Santos
1,*,
Tiago do Prado Paim
1,
Luizmar Peixoto dos Santos
1,
Estenio Moreira Alves
2,
Flavio Lopes Claudio
2,
Guido Calgaro Junior
2,
Patrick Bezerra Fernandes
1 and
Paulo Alexandre Perdomo Salviano
2
1
Instituto Federal de Educação, Ciência e Tecnologia Goiano Campus Rio Verde, Rodovia Sul Goiana, Km 01, Zona Rural, Rio Verde 75.901-970, GO, Brazil
2
Instituto Federal de Educação, Ciência e Tecnologia Goiano Campus Iporá, Avenida Oeste, n.350, Parque União, Iporá 76200-000, GO, Brazil
*
Author to whom correspondence should be addressed.
Agriculture 2023, 13(3), 637; https://doi.org/10.3390/agriculture13030637
Submission received: 2 February 2023 / Revised: 24 February 2023 / Accepted: 3 March 2023 / Published: 8 March 2023
(This article belongs to the Topic Innovation and Solution for Sustainable Agriculture)
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Abstract

:
Crop–livestock integrated production systems (CLISs) combine cash-crop production and forage production in succession. There are plenty of options of forage cultivars with differences in production aspects and seeds cost, and there is little information on how the choice of forage cultivar can affect the results of a CLIS. We hypothesized that different forage cultivars can have important economic impacts on production systems. Thus, we evaluated the two-year economic results of using three forage species in a CLIS: (1) Urochloa ruziziensis; (2) Megathyrsus maximus cv. BRS Zuri e; and (3) Megathyrsus maximus cv. BRS Tamani. The system was evaluated during 2018 and 2019 with no-tillage soybean (Glycine max) cultivation from November to March and grazing of cattle from May to August. The seed costs were, on average, USD 25.27 ha−1 for Ruziziensis grass, USD 39.97 ha−1 for Zuri guinea grass, and USD 64.13 ha−1 for Tamani guinea grass. Animal production varied from 96.4 to 147.5 kg of live weight per hectare per year and mean two-year soybean yields varied from 3849 to 4217 kg per hectare, both without differences between forage cultivars. However, the lowest values for animal and soybean yields were obtained with Ruziziensis grass, and the highest were obtained with Zuri grass. Thus, Zuri guinea grass presented a net income (NI) of USD 1039.87 ha−1 with an annual return on equity (ROE) equal to 11.19%, while Ruziziensis grass obtained an NI equal to USD 612.65 ha−1 with an ROE of 6.47%, demonstrating the economic impact of forage resource choice in CLISs. Therefore, the choice of forage cultivars adequate for the conditions of an individual farm can correspond to an increase of 69.7% in net income, which highlights the importance of continuing efforts to develop new cultivars and the simultaneous evaluation of these cultivars in different production scenarios in order to better recommend forage genetic resources for particular production environments.

1. Introduction

One of the biggest challenges in agriculture in the 21st century is boosting primary production (grains and forage biomass) with less prohibitive fixed costs and environmental preservation. Therefore, it is necessary to develop cultivation strategies that will allow the use of available abiotic resources in more sustainable ways. Crop–livestock integrated systems can increase land-use efficiency in a more rational way, since, when a strategic combination of different functional groups of plants is selected, it is possible to maximize production with the same resources [1,2].
Crop–livestock integrated systems (CLISs) are sustainable food production systems with no-till cultivation of cash crops and forage species in consortium, rotation, and/or succession. CLISs can increase food production per hectare, which increases the economic return and at the same time decreases the pressure for deforestation for food production systems [3,4]. Crop diversity improves soil physical, chemical, and biological parameters [5]. Simultaneously, multiple sources of income enable better cash flow and decrease risk [6,7]. CLISs with soybean–pasture succession yield grains in regular harvest seasons and good quality forage during the dry season of the year (harvest off-season) and are associated with high biomass input and good soil coverage following good no-tillage practices [8,9,10,11].
In CLISs, Dias et al. [12] found that the strategic diversification of different plant genera and species throughout the year promoted greater nutrient cycling when compared to a conventional system, thus contributing to production sustainability [13]. In a crop rotation system, over a period of 12 years, Rigon et al. [14] verified that the consortium of Urochloa ruziziensis with sorghum (Sorghum bicolor) generated a high-quality post-harvest residue, increasing phosphorus recycled by soybean (Glycine max).
Muniz et al. [1] verified the effects of the cultivation of two grasses (Urochloa brizantha cv. ‘BRS Paiaguás’ and Megathyrsus maximus cv. ‘BRS Tamani’) intercropped with corn and showed that the intercropping systems provided greater soil coverage and nutrient cycling in relation to maize in monoculture; thus, intercropping favors greater soybean productivity. The authors showed that grass exhibited a greater fraction of carbon and nitrogen, slowing the straw decomposition process and providing an increase in organic matter and nutrients in the upper layers of soil.
Carvalho et al. [6], evaluating livestock production in agriculture systems over 9 years, showed increments in yield of 3,4%, 4,7%, 10,4%, and 10,8% in soybean, common beans, rice, and corn, respectively. Over 9 years of experiments, these authors showed that a traditional soybean system (exclusive soybean production) yielded 2.940 kg ha−1, on average, while the CLIS (soybean and beef cattle production) yielded 4.860 kg ha−1, converting beef into soybean equivalent.
Economic analyses of CLISs have great importance in guiding the decisions of farmers and for the definition of research priorities. Most CLIS studies evaluate only productivity parameters combined with soil properties and animal production aspects, and there is a lack of economic evaluation of whole systems [15]. Livestock rearing in a CLIS can be an opportunity for diversification and income [5], but it can also represent a significant increase in the cost of infrastructure (water tanks, fences, and corrals) and equity due the animal value, increasing the financial risk of the business [7]. The use of economic-based decision-making processes can enhance economic sustainability by economy of scope, due to a lower cost for the same product and lower risk due to product variety [16].
The cost of seeds and forage production potential differ according to forage cultivars, and it is not clear how the choice of forage cultivars can affect the economic results of production systems. There are more than 30 cultivars of forage species commercially available in Brazil nowadays. Most of these cultivars are from two genera: Urochloa spp. and Megathyrsus maximus. Urochloa ruziziensis is the main species used in CLISs due to the low seed cost, higher glyphosate sensitivity, fast growth with high biomass production, and soil coverage [17,18,19]. Cultivars of Megathyrsus maximus are, in general, associated with higher seed costs and higher forage/biomass production [20]. Megathyrsus maximus cv. BRS Tamani have a small size (20 to 60 cm in sward height), with high leaf-to-stem proportions, high nutritional value, and resistance to pasture leafhoppers [19,21]. Megathyrsus maximus cv. BRS Zuri, meanwhile, have a tall size (60 to 100 cm in sward height), fast growth, higher biomass production, high nutritional value, and resistance to spittlebugs in pastures [22]. However, these species are not widely used in CLISs due to the higher glyphosate doses required for desiccation and possible impairments to soybean no-till sowing in subsequent seasons due to the elevated amounts of biomass coupled with tussocks in the field. Thus, we hypothesize that the choice of cultivars that are adequate for environmental and farm conditions can enhance the economic results of CLISs, which could justify more research for improved decision-making processes regarding CLISs.
The aim of this study was to evaluate the production and economic consequences of choosing different forage cultivars in a CLIS. Therefore, the main points evaluated were: (I) animal production; (II) residual biomass and canopy structure; (III) soybean yield; and (IV) the economic results of the CLIS.

2. Materials and Methods

The experiment was conducted on Encanto Farm (privately owned), located in Montes Claros de Goiás, GO, Brazil (15°43′54.69″ S and 51°37′13.09″ O). The farm is in Brazilian savanna with an AW climate, according to the Köppen climate classification (Köppen and Geiger [23]). Normally, there is no precipitation from May to September in this region.
The crop–livestock integrated system (CLIS) used here consisted of soybean cultivation during the summer (November to March) and pasture during the winter, with a grazing period from May to August. Three different forages were used in this production system during a two-year trial: (1) Urochloa ruziziensis (Syn. Brachiaria ruziziensis); (2) Megathyrsus maximus cv. BRS Zuri (Syn. Panicum maximum cv. BRS Zuri); and (3) Megathyrsus maximus cv. BRS Tamani (Syn. Panicum maximum cv. BRS Tamani), called hereafter Ruziziensis grass, Zuri guinea grass, and Tamani guinea grass, respectively. The experimental area (Figure 1) was 44.43 ha, divided into three paddocks for each of the grasses (15.26 ha for Ruziziensis grass, 16.67 ha for Zuri guinea grass, and 12.5 ha for Tamani guinea grass).

2.1. Animal and Forage Production

Soybean no-till sowing was performed in November of each year using the cultivar BMX Bônus. The plant protection management was the same for all the treatments and followed the agronomical recommendations. Soybean harvest was performed in March. The following production components were determined: grain yield (kg ha−1), stands (plants m−2), and yield per plant (g plant−1). Manual sampling was performed at five points for each repetition, with sampling of two meters of two soybean rows (50 cm between rows). For soybean yield, all plants sampled in these two rows were threshed, the grains were weighed, and the moisture contents were measured. Then, grain yield was calculated by adjusting to 13% of moisture. To determine stands, the numbers of plants in the two 2-m rows were counted. At the side of the sampling point, five random plants were sampled, and later the numbers of pods and grains were manually measured to obtain data on production per plant.
Pasture was overseeded on soybean crops at R5.5 stage using 4 kg ha−1 of pure viable seeds of Ruziziensis grass, Tamani grass, and Zuri grass. Therefore, during the period between the grass seeding and the soybean harvest, an intercropping system was established. Grazing began when the pasture had reached the recommended sward height for grazing (30 cm, 65 cm, and 80 cm, respectively) [24,25,26].
The livestock phase involved setting up an electric fence, water tanks, and troughs (30 cm per animal) for supplementation. Heifers at 20 months of age and with an average weight of 290 kg were used. The numbers of animals used in 2018 and 2019 were, respectively, 28 and 46 for Ruziziensis grass, 46 and 42 for Tamani guinea grass, and 21 and 39 for Zuri guinea grass. The animals were weighed at the beginning and at the end of the experiment, and intermediated weighing sessions were carried out at each 28 days of grazing. The animals (testers) remained in the same paddocks during the whole experimental period, using a continuous stocking grazing system.
Forage dry mass was measured every 28 days [27,28]. The forage evaluation was performed, cutting 1 m² at five random points in each paddock. The cutting was performed with a pruner (STIHL® HS 45) at 10 cm above ground level. The forage available was harvested, weighed, and placed in a dry oven (55 °C) for 72 h, then weighed again, determining the available forage mass in kg of dry matter per hectare. The stocking rate was adjusted in each paddock based on live weight and forage dry matter mass every 28 days.
The animals received increasing supplementation planning during the 2018 grazing period, starting with 0.3% of live weight (LW), then 0.5% of LW and 1.5% of LW. During the 2019 grazing period, animals received mineral supplementation ad libitum (mean consumption of 80 g per animal per day). In the 2018 grazing period, heifers started grazing on May 2 for Zuri guinea grass and Tamani guinea grass and on June 5 for Ruziziensis grass. Grazing ended on July 04 for Zuri guinea grass and on August 30 for ‘BRS Tamani’ and Ruziziensis grass. Therefore, the numbers of grazing days in 2018 were 63, 120, and 86 for ‘BRS Zuri’, ‘BRS Tamani’, and Ruziziensis grass, respectively. In the 2019 grazing period, heifers started grazing on May 6 for all three grasses, and grazing ended on July 29 for Zuri and Ruziziensis grass and on September 04 for Tamani guinea grass. Therefore, Tamani guinea grass had 121 grazing days and the other grasses had 84 grazing days.
Stocking rate was defined in animal units (AU = 450 kg LW) per hectare. The average live weight gain per day (LWG) was calculated as the difference between initial and final live weights divided by the number of days between weighings. The paddock live weight gain was obtained from the LWG multiplied by the number of animals in the paddock at that period.
Fattening score was determined by visual appraisal at each weighing, following similar procedures to those described in Ayres et al. [29]. Heifers with adequate fattening scores (higher than 4 on a 1–5 scale) were sent to a commercial slaughterhouse, where carcass weights were recorded. Animals were commercialized in five rounds in 2018 and in four rounds in 2019.

2.2. Economic Analyses

Economic analyses were split into crop and livestock phases. Total cost (TC) consisted of the sum of livestock costs and soybean crop costs. The direct costs of the operation, considering all mechanized procedures (from pasture sowing, crop seeding, management, and harvest), animal purchases, and infrastructure purchases and installations, were evaluated. In the crop phase, all supplies expended were recorded. As the management during crop phase was equal to all forage cover crops, the soybean costs were the same for all treatments.
Operational costs, livestock supplements, crop cultivation inputs (such as fertilizers, pesticides, and seeds), animal values, and land values were measured. Labor expended with livestock management was determined by the average income per hour of cowboys in the region (USD 1.50 h−1). The number of hours expended in each year was recorded and divided by the number of animals in the experiment, resulting in a cost of USD 0.10 head−1 day−1 (2018) and USD 0.038 head−1 day−1 (2019). The supplementation strategy used in 2018 resulted in more hours expended than in 2019.
Animal supplements offered in 2018 totaled 449.02 kg ha−1, 455.41 kg ha−1, and 509.02 kg ha−1, for ‘BRS Tamani’, ‘BRS Zuri’, and Ruziziensis grass, respectively. In 2019, the mineral supplements offered totaled 33.94 kg ha−1 for Tamani guinea grass, 66.71 kg ha−1 for Zuri-grass, and 72.87 kg ha−1 for Ruziziensis grass. The supplementation used represented the main strategies used by farmers in CLISs in Central Brazil.
Pasture seed costs were determined by the total seed weights expended and the real prices paid out by the farmer. ‘BRS Zuri’, ‘BRS Tamani’, and Ruziziensis grass seed costs were USD 10.13, 17.23, and 7.87 per kg of pure viable seeds (PVS) in 2018 and USD 9.85, 14.83, and 4.77 kg−1 in 2019, respectively.
Animal purchase costs were USD 382.12 and USD 383.18 per heifer in 2018 and 2019. The mean selling prices were USD 2.25 and USD 2.43 kg−1 per kilogram of carcass in 2018 and 2019, respectively. The cost of FUNDEPEC-GO (livestock development fund) was USD 1.12 animal−1, and other taxes (FUNRURAL and animal transit passes) for commercialization were added to the total livestock phase costs.
Total gross income (GI) was calculated on the basis of income from soybean commercialization (the amount produced multiplied by the mean selling price obtained by the farmer) and livestock (the difference between the selling and buying prices for each animal). Total net income (NI) was determined by the difference between total gross income (GI) and total cost (TC).
The equity value consisted of land value and circulating capital (animals plus total cost). Land values were determined by a price survey in the region, obtaining an average land value of USD 2682.04 ha−1.
The return on equity per month (ROEm) was calculated by taking the net income of each phase (NI) divided by the total equity multiplied by the number of months for each phase, with four months for the livestock phase and eight months for the soybean phase. Then, the ROE per year was calculated: ROEy = ((ROEm + 1)^12)−1, where ROEm = return on equity per month and ROEy = return on equity per year.
The results were submitted to an analysis of variance considering the effects of the three forage species, and, if an effect was significant (p < 0.05), the means were compared by least significant differences (considered to be equal to twice the standard deviation). Analyses used the car [30] and emmeans [31] packages in R software v.4.1.0 [32].

3. Results

Livestock and Crop Yields

There was no difference in mean forage availability in 2018 between the forage species. In 2019, Zuri grass had more forage availability compared to the other species (Figure 2). Regarding the two-year averages, Zuri grass presented higher forage availability.
In 2018, Tamani guinea grass had a lower stocking rate than the other species (Table 1). In 2019, there was no difference in animal grazing parameters. On average, Zuri grass had a higher stocking rate compared to Tamani guinea grass, while Ruziziensis grass was positioned in an intermediate position, not differing from either (Table 1).
There was no effect of the forage species as cover crop on soybean yield (Table 2). Regarding plant stands, Ruziziensis grass showed higher numbers than the other species, but this did not affect soybean yield.

Economic Analyses

Tamani grass had the highest seed cost (USD 64.13 ha−1), followed by Zuri guinea grass (USD 39.97 ha−1) and Ruziziensis grass, which had the lowest (USD 25.27 ha−1). Therefore, the adoption of Megathyrsus maximus represented increments of 58.7% (Zuri guinea grass) and 154.4% (Tamani guinea grass) in seed investment relative to Ruziziensis grass (the main forage cultivar used in CLISs). Seed expenditure represented 25% to 43% of the cost of the livestock phase, considering the average of the two years (Figure 3). Labor represented only 5 to 7% of the total cost of the livestock phase, while animal supplementation represented 52% to 69% of the cost of the livestock phase (Figure 3).
There were no statistical differences in soybean production costs and production due to the different forage species used previously in the two-year evaluation (Table 2). Return on equity (yearly basis) for the soybean phase varied from 11.69% with Ruziziensis grass in the system to 17.77% with Zuri guinea grass in the system. In 2018, Ruziziensis grass presented a negative net income in the livestock phase, while Zuri guinea grass presented a 13.66% return on equity on a yearly basis (Table 3). In 2019, Tamani grass presented an ROE per year equal to 10.16%, while those of the other species were lower than 7% (6.99% for Ruziziensis grass and 6.5% for Zuri guinea grass). On average, return on equity (yearly basis) varied from 3.08% for Ruziziensis grass to 8.57% for Tamani guinea grass to 10.08% for Zuri grass during the livestock phase (Table 3).
Overall, the net income and return on equity were positive for all treatments (Table 4). Zuri guinea grass had an 11.19% ROEy, while Tamani grass and Ruziziensis grass had ROEy values of 9.51% and 6.47%, respectively.

4. Discussion

Zuri guinea grass had higher mean forage availability during the grazing period compared to the other species, 13.9% and 20.2% higher than Ruziziensis grass and Tamani guinea grass, respectively. This cultivar is known for its fast growth and high efficiency of use of available abiotic resources [33]. The higher forage availability allowed a higher stocking rate (2.03 AU ha−1), which was 30.54% more than that of Ruziziensis grass (1.41 AU ha−1). Otherwise, Tamani guinea grass had longer grazed periods in both years, which demonstrated the difference between tall-size and small-size types of Megathyrsus maximus.
Both Megathyrsus maximus cultivars (BRS Tamani and BRS Zuri) had higher live weight gains per hectare (LWGH) than Urochloa ruziziensis in the first year. In the second year, the differences between forage species narrowed, but the order was the same: Zuri guinea grass, Tamani guinea grass, Ruziziensis grass. At the end, on average, the Zuri guinea grass system produced 147.5 kg of LWGH, while Ruziziensis grass yielded 96.4 kg, which represents a 53% increment in animal weight produced per area. Similar results were observed by Dias et al. [12], which reinforces the need to explore these forage genetic resources in crop–livestock integrated systems.
There was no difference in soybean yield over the two years (Table 2), which reinforces that, with good management practices, cultivars of Megathyrsus maximus can be utilized in crop–livestock integrated systems without losses to main crops [34,35].
Supplement cost represented an important part of livestock cost, especially in the first year, when a strategy of protein-energetic supplementation was used. Santos et al. [36] also observed that supplement costs represented the highest proportions of livestock costs in crop–livestock integrated systems.
In general, Megathyrsus had higher seed costs than Urochloa, which can partially justify the intense use of Urochloa ruziziensis in crop–livestock systems in Brazil, as the use of Zuri guinea grass and Tamani guinea grass represented increments of 58.7% and 154.4% in investment in seeds, respectively. Thus, if a farmer does not expect important yield differences between forage cultivars, it is justified to choose the low-cost option. This shows the importance of studies verifying the productive and economic aspects of choices of forage genetic resources. Some previous studies have shown the higher forage/biomass production of Megathyrsus in crop–livestock integrated systems [35].
Investments for animal purchase represented 26% of the total equity of the system. The resource requirements for animals present one of the main challenges for livestock production in integrated systems. Compared to traditional livestock systems, the stocking rate is three to four times higher; consequently, the investment in animals per hectare is proportionally higher. At the same time, the livestock phase of the system was not always profitable and demonstrated huge variation in the three treatments over the two years. Ruziziensis grass in the first year had a negative net income, for example. Thus, the introduction of animals in production systems is an opportunity for increasing and diversifying income; however, this represents an increase in financial risk due to the increase in capital applied even more nowadays with increased price volatility of this commodity [37,38]. Poffenbarger [39] observed that CLISs had higher costs (higher investments) compared to conventional systems in the US between 2008 and 2015. These authors also concluded that they were associated with higher animal acquisition and labor costs. However, these authors showed better economic results for CLISs compared to conventional systems in the long term.
The return on equity for the whole system over the two years equaled 6.5%, 9.6%, and 11.2% for Ruziziensis grass, Tamani guinea grass, and Zuri guinea grass, respectively. Therefore, a simple choice of forage genetic resources can be responsible for a 69.7% increment in net income. This result is a consequence of the higher animal and soybean yields with moderate seed costs. Other studies have shown that Zuri guinea grass is a very fast biomass producer, with good animal performance and stocking rates in high-fertility soils [40,41,42].

5. Conclusions

Crop–livestock integrated systems can enable good economic results despite the elevated investments in animals they require. The choice of the forage genetic resources to be used is very important and can have an important impact on economic results: in the present study, the simple choice of forage genetic resources was responsible for a 69.7% increment in net income. Therefore, the evaluation of the production potentials of different forage species is essential to support the decision-making processes of farmers. Moreover, it is important to select species to be used in crop rotation systems with high biomass inputs coupled with no-till sowing to obtain sustainable production systems (environmentally and economically). For the region of this study, we highlight Megathyrsus maximus cv. ‘BRS Zuri’ as an important option for winter grazing as part of a crop–livestock integrated system with a crop rotation system.

Author Contributions

Conceptualization: T.d.P.P., E.M.A. and D.C.S.; Experiments and data collection: V.N.L., L.P.d.S., F.L.C. and G.C.J.; Data curation: E.M.A. and P.A.P.S.; Statistical analysis—design and performance: T.d.P.P.; Writing—first draft: V.N.L.; Writing—review and final version: D.C.S., T.d.P.P., E.M.A., P.B.F. and P.A.P.S. All authors have read and agreed to the published version of the manuscript.

Funding

Coordination for the Improvement of Higher Education Personnel (CAPES) is acknowledged for support through a graduate scholarship program; the National Council for Scientific and Technological Development (CNPq) (grant nos. 400901/2019-6 and 409400/2021-1) and the Federal Institute of Education, Science and Technology Goiano (IF Goiano) (grant no. 19/2021) are also acknowledged.

Institutional Review Board Statement

The animal study protocol was approved by the Institutional Ethics Committee of the Goiano Federal institute (CEUA/IF GOIANO) (protocol code 5700050321; approved on 15 April 2021).

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

The authors acknowledge the contribution of the regional farmers’ cooperative COMIGO for support in the first steps of the partnership with the commercial farms. We acknowledge the owner (Pedro Celso Gonçalves) and workers of Fazenda Encanto for all the support for the research. The authors thanks Instituto Federal Goiano for supporting the field research and publication.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Muniz, M.P.; Costa, K.A.D.P.; Severiano, E.D.C.; Bilego, U.O.; Almeida, D.P.; Neto, A.E.F.; Vilela, L.; Lana, M.A.; Leandro, W.M.; Dias, M.B.D.C. Soybean yield in integrated crop livestock system in comparison to soybean maize succession system. J. Agric. Sci. 2021, 159, 188–198. [Google Scholar]
  2. Amadori, C.; Dieckow, J.; Zanatta, J.A.; Moraes, A.; Zaman, M.; Bayer, C. Nitrous oxide and methane emissions from soil under integrated farming systems in southern Brazil. Sci. Total Environ. 2022, 828, 154–555. [Google Scholar] [CrossRef]
  3. Ruviaro, C.F.; da Costa, J.S.; Florindo, T.J.; Rodrigues, W.; de Medeiros, G.I.B.; Vasconcelos, P.S. Economic and environmental feasibility of beef production in different feed management systems in the Pampa biome, southern Brazil. Ecol. Indic. 2016, 60, 930–939. [Google Scholar] [CrossRef]
  4. Gléria, A.A.; Silva, R.M.; Santos, A.P.P.; Santos, K.J.G.; Paim, T.P. Produção de bovinos de corte em sistemas de Integração Lavoura e Pecuária. Arch Zootec 2017, 66, 141–150. [Google Scholar] [CrossRef]
  5. Ambus, J.V.; Reichert, J.M.; Gubiani, P.I.; de Faccio Carvalho, P.C. Changes in composition and functional soil properties in long-term no-till integrated crop-livestock system. Geoderma 2018, 330, 232–243. [Google Scholar] [CrossRef]
  6. Carvalho, P.C.D.F.; Peterson, C.A.; Nunes, P.A.D.A.; Martins, A.P.; de Souza Filho, W.; Bertolazi, V.T.; Anghinoni, I. Animal production and soil characteristics from integrated crop-livestock systems: Toward sustainable intensification. J. Anim. Sci. 2018, 96, 3513–3525. [Google Scholar] [CrossRef] [PubMed]
  7. Rego, C.A.R.D.M.; Muniz, L.C.; Reis, V.R.R.; Cantanheide, I.S.D.L.; Costa, B.P.; Marques, E.D.O.; Oliveira, P.S.R.D. economic analysis of the implementation of different systemsde of crop-livestock-forestry integration in the municipality of Pindaré-Mirim, Maranhão. Sodebras 2018, 13, 146. [Google Scholar]
  8. Alves, B.R.; Madari, B.E.; Boddey, R.M. Integrated crop-livestock-foresty sustems: Prospects for a sustainable agricultural intensification. Nutr. Cycl. Agroecosyst. 2017, 108, 1–4. [Google Scholar] [CrossRef]
  9. Asai, M.; Moraine, M.; de Wit, J.; Hoshide, A.K.; Martin, G. Critical factors for crop-livestock integration beyond the farm level: A crossanalysis of worldwide case studies. Land Use Policy 2018, 73, 84–194. [Google Scholar] [CrossRef]
  10. Schuster, M.Z.; Lustosa, S.B.C.; Pelissari, A.; Harrison, S.K.; Sulc, R.M.; Deiss, L.; Lang, C.R.; Carvalho, P.C.F.; Gazziero, D.L.P.; De Moraes, A. Optimizing forage allowance for productivity and weed management in integrated croplivestock systems. Agron. Sustentar. Dev. 2019, 39, 18. [Google Scholar] [CrossRef]
  11. Nepstad, L.S.; Gerber, J.S.; Hill, J.D.; Dias, L.C.P.; Costa, M.H.; West, P.C. Pathways for recent Cerrado soybean expansion: Extending the soy morato-rium and implementing integrated crop livestock systems with soybeans. Environ. Res. Lett. 2019, 14, 029–044. [Google Scholar] [CrossRef]
  12. Dias, M.B.C.; Costa, K.A.P.; Severiano, E.C.; Bilego, U.O.; Almeida, D.P.; Brand, S.C.; Vilela, L.; Furtini-Neto, A.E. Brachiaria and Panicum maximum in an integrated crop-livestock system and a second-crop maize system in succession with soybean. J. Agric. Sci. 2020, 1, 1–12. [Google Scholar] [CrossRef]
  13. Costa, N.; Andreotti, M.; Crusciol, C.A.C.; Pariz, C.M.; Bossolani, J.W.; De Castilhos, A.M.; Nascimento, C.A.C.D.; Lima, C.G.D.R.; Bonini, C.D.S.B.; Kuramae, E. Can Palisade and Guinea Grass Sowing Time in Intercropping Systems Affect Soybean Yield and Soil Chemical Properties? Front. Sustain. Food Syst. 2020, 4, 81. [Google Scholar] [CrossRef]
  14. Rigon, J.P.G.; Crusciol, C.A.C.; Calonego, J.C.; Pavinato, P.S.; Azevedo, A.C.; Rosolem, C.A. Intensive crop rotations and residue quality increase soil phosphorus lability under long-term no-till in tropical soils. Soil Tillage Res. 2022, 223, 105446. [Google Scholar] [CrossRef]
  15. Soler, R.; Peri, P.L.; Bahamonde, H.; Gargaglione, V.; Ormaechea, S.; Herrera, A.H.; Jardon, L.S.; Lorenzo, C.; Pastur, G.M. Assessing Knowledge Production for Agrosilvopastoral Systems in South America. Rangel. Ecol. Manag. 2018, 71, 637–645. [Google Scholar] [CrossRef]
  16. Martha Júnior, G.B.; Alves, E.; Contini, E. Dimensão econômica de sistemas de integração lavoura-pecuária. Pesqui. Agropecuária Bras. 2011, 46, 1117–1126. [Google Scholar] [CrossRef]
  17. Carvalho, A.M.; De Souza, L.L.P.; Guimarães Junior, R.; Alves, P.C.A.; Vivaldi, L.J. Plantas de cobertura com potencial de uso para sistemas de integração lavoura-pecuária na região do Cerrado. Pesq. Agropec. Bras. 2011, 46, 1200–1205. [Google Scholar] [CrossRef]
  18. Tegegn, A.; Kyalo, M.; Mutai, C.; Hanson, J.; Asefa, G.; Djikeng, A.; Ghimire, S. Genetic diversity and population structure of Brachiaria brizantha (A. Rich.) stapf accessions from Ethiopia. Afr. J. Range Forage Sci. 2019, 36, 129–133. [Google Scholar] [CrossRef]
  19. Dias, M.B.D.C.; Costa, K.A.D.P.; Severiano, E.D.C.; Bilego, U.O.; Vilela, L.; de Souza, W.F.; de Oliveira, I.P.; da Silva, A.C.G. Cattle performance with Brachiaria and Panicum maximum forages in an integrated crop-livestock system. Afr. J. Range Forage Sci. 2021, 39, 230–242. [Google Scholar] [CrossRef]
  20. Jank, L.; Barrios, S.C.; Do Valle, C.B.; Simeão, R.M.; Alves, G.F. The value of improved pastures to Brazilian beef production. Crop Pasture Sci. 2014, 65, 1132–1137. [Google Scholar] [CrossRef]
  21. Tesk, C.R.M.; Cavalli, J.; Pina, D.S.; Pereira, D.H.; Pedreira, C.G.S.; Jank, L.; Sollenberger, L.E.; Pedreira, B.C. Herbage responses of Tamani and Quênia guinea grasses to grazing intensity. Agron. J. 2020, 112, 2081–2091. [Google Scholar] [CrossRef]
  22. Lima, G.C.; Hungria, M.; Nogueira, M.A.; Filho, M.C.M.T.; Moreira, A.; Heinrichs, R.; Filho, C.V.S. Yield, yield components and nutrients uptake in zuri guinea grass inoculated with plant growth-promoting bacteria. Int. J. Innov. Educ. Res. 2020, 8, 103–124. [Google Scholar] [CrossRef]
  23. Köppen, W. Das Geographische System der Klimate; Gebrüder Borntraeger: Berlin, Germany, 1936; pp. 1–44. [Google Scholar]
  24. Dias Filho, M.B. Formação e manejo de pastagens. Comunicado Técnico 235, Embrapa Amazônia Oriental 2012. Available online: http://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/937485 (accessed on 1 October 2022).
  25. Costa, J.A.A.; Queiroz, H.P. Régua de Manejo de Pastagens—Edição Revisada; Embrapa: Campo Grande, Brazil, 2017; 7p. [Google Scholar]
  26. Fidalski, J.; Tormena, C.A.; Alves, S.J. Intervalo hídrico ótimo de um latossolo vermelho distrófico, após o primeiro período de pastejo contínuo de Brachiaria ruziziensis, em sistema integração lavoura-pecuária. R. Bras. Ci. Solo 2013, 37, 775–783. [Google Scholar] [CrossRef]
  27. Silva, D.J.; Queiroz, A.C. Análise de Alimentos: Métodos Químicos e Biológicos, 3rd ed.; UFV 235: Viçosa, Brazil, 2002. [Google Scholar]
  28. Detmann, E.; Souza, M.A.D.; Valadares Filho, S.D.C.; Queiroz, A.C.D.; Berchielli, T.T.; Saliba, E.D.O.S.; Cabral, L.D.S.; Pina, D.D.S.; Ladeira, M.M.; Azevedo, J.A.G. Métodos para análise de alimentos. Visconde Do Rio Branco Suprema 2012, 17, 214. [Google Scholar]
  29. Ayres, H.; Ferreira, R.M.; Torres-Júnior, J.R.D.S.; Demétrio, C.G.B.; de Lima, C.G.; Baruselli, P. Validation of body condition score as a predictor of subcutaneous fat in Nelore (Bos indicus) cows. Livest. Sci. 2009, 123, 175–179. [Google Scholar] [CrossRef]
  30. Fox, J.; Weisberg, S. An {R} Companion to Applied Regression, 3rd ed; Sage: Thousand Oaks, CA, USA, 2019; Available online: https://socialsciences.mcmaster.ca/jfox/Books/Companion/ (accessed on 1 July 2022).
  31. Length, R. _emmeans: Estimated Marginal Means, aka Least-Squares Means_. R package version 1.8.0. 2022. Available online: https://CRAN.R-project.org/package=emmeans (accessed on 1 July 2022).
  32. R CORE TEAM. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing, Vienna, Austria. 2020. Available online: https://www.R-project.org/ (accessed on 1 July 2022).
  33. Silva, S.C.; Sbrissia, A.F.; Pereira, L.E.T. Ecophysiology of C4 forage grasses—Understanding plant growth for optimising their use and management. Agriculture 2015, 5, 598–625. [Google Scholar] [CrossRef]
  34. Almeida, R.E.M.; Gomes, C.M.; Lago, B.C.; Oliveiras, S.M.; Pierozan Junior, C.; Faravin, J.L. Corn yield, forage production and quality affected by methods of intercropping corn and Panicum maximum. Pesq. Agropec. Bras. 2017, 52, 170–176. [Google Scholar] [CrossRef]
  35. Momesso, L.; Crusciol, C.A.C.; Soratto, R.P.; Vyn, T.; Tanaka, K.S.; Costa, C.H.M.; Costa, J.F.N.; Cantarella, H. Impacts of nitrogen management on no–till maize production following forage cover crop. Agron. J. 2019, 111, 639–649. [Google Scholar] [CrossRef]
  36. Santos, A.A.P.D.; Vidigal Filho, A.L.; Vidigal, L.L.D.V.; De Souza, V.L.; De Figuereido, A.M.B.; Piacentini, M.T.S. Análise da rentabilidade do sistema semi-intensivo de engorda de bovinos semiconfinamento. Res. Soc. Dev. 2022, 11, e10011427128. [Google Scholar] [CrossRef]
  37. Hoag, D.L. Applied Risk Management in Agriculture; CRC Press: Boca Raton, FL, USA, 2010; pp. 419–424. ISBN 9781439809730. [Google Scholar]
  38. Girdžiūtėa, L. Risks in agriculture and opportunities of their integrated evaluation. Procedia Soc. Behav. Sci. 2012, 62, 783–790. [Google Scholar] [CrossRef]
  39. Poffenbarger, H.; Artz, G.; Dahlke, G.; Edwards, W.; Hanna, M.; Russell, J.; Sellers, H.; Liebman, M. An economic analysis of integrated crop-livestock systems in Lowa, U.S.A. Agric. Syst. 2017, 157, 51–69. [Google Scholar] [CrossRef]
  40. Soares, K.A.R.S.C. Avaliação Nutricional da Silagem de Capim-Zuri (Panicum maximum cv. BRS Zuri) Contendo Diferentes Aditivos. Sinop-MT: Dissertação (Mestrado)—2017; Universidade Federal de Mato Grosso, Instituto de Ciências Agrárias e Ambientais, Programa 50 de Pós-Graduação em Zootecnia: Cuiabá, Brazil, 2017. [Google Scholar]
  41. Braga, G.J.; Maciel, G.A.; Guimarães Junior, R.G.; Ramos, A.K.B.; Carvalho, M.A.; Fernades, F.D.; Fonseca, C.E.L.; JANK, L. Performance of young Nellore bulls on guinea grass pastures under rotational stocking in the Brazilian Cerrado. Trop. Grassl. Forrajes Trop. 2019, 7, 214–222. [Google Scholar] [CrossRef]
  42. Silva, E.B.; Carneiro, M.S.D.S.; Furtado, R.N.; Lopes, M.N.; Braga, M.D.M. Chemical composition of Panicum maximum ‘BRS Zuri’ subjected to levels of salinity and irrigation depths. Rev. Ciênc. Agron 2020, 51, 1–10. [Google Scholar] [CrossRef]
Agriculture 13 00637 g001 550
Figure 1. Experimental field on Encanto Farm in Montes Claros de Goiás, Goiás State, Brazil.
Figure 1. Experimental field on Encanto Farm in Montes Claros de Goiás, Goiás State, Brazil.
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Agriculture 13 00637 g002 550
Figure 2. Mean forage dry matter mass availability during the grazing phase of the crop–livestock integrated system using different forage species: Urochloa ruziziensis, Megathyrsus maximus cv. BRS Zuri, and Megathyrsus maximus cv. BRS Tamani. Different letters in the same year means statistical differences between forage species.
Figure 2. Mean forage dry matter mass availability during the grazing phase of the crop–livestock integrated system using different forage species: Urochloa ruziziensis, Megathyrsus maximus cv. BRS Zuri, and Megathyrsus maximus cv. BRS Tamani. Different letters in the same year means statistical differences between forage species.
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Agriculture 13 00637 g003 550
Figure 3. Cost composition (USD per hectare) of the livestock phase for 2018 (A), 2019 (B), and the two-year average (C).
Figure 3. Cost composition (USD per hectare) of the livestock phase for 2018 (A), 2019 (B), and the two-year average (C).
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Table
Table 1. Livestock production in the crop–livestock integrated system over two years (2018 and 2019) using three forage species for winter grazing: Urochloa ruziziensis, Megathyrsus maximus cv. BRS Zuri, and Megathyrsus maximus cv. BRS Tamani.
Table 1. Livestock production in the crop–livestock integrated system over two years (2018 and 2019) using three forage species for winter grazing: Urochloa ruziziensis, Megathyrsus maximus cv. BRS Zuri, and Megathyrsus maximus cv. BRS Tamani.
PastureMLW
(kg)		SR
(kg ha−1)		SR
(AU ha−1)		LWG
(kg dia−1)		LWGH
(kg ha−1)
2018
Ruziziensis358.4 a578.2 a1.28 ab0.701 a96.70 b
BRS Zuri326.8 a943.6 a2.10 a0.975 a180.65 a
BRS Tamani356.2 a444.3 b0.99 b0.793 a133.92 a
2019
Ruziziensis320.0 a694.0 a1.54 a0.532 a96.07 a
BRS Zuri330.8 a881.8 a1.96 a0.422 a114.44 a
BRS Tamani326.4 a522.8 a1.16 a0.544 a100.56 a
Mean
Ruziziensis339.0 a636.1 ab1.41 ab0.616 a96.4 a
BRS Zuri329.0 a912.7 a2.03 a0.699 a147.5 a
BRS Tamani341.0 a483.6 b1.07 b0.668 a117.2 a
SD16.52200.500.450.20332.85
Means followed by different letters in the column represent differences higher than two times the standard deviation of the variable. MLW: mean live weight; SR: stocking rate; LWG: live weight gain per day; LWGH: live weight gain per hectare; SD: standard deviation.
Table
Table 2. Soybean yield and production components in the crop–livestock integrated system over two years (2018 and 2019) using three different forage species as cover crops: Urochloa ruziziensis, Megathyrsus maximus cv. BRS Zuri, and Megathyrsus maximus cv. BRS Tamani.
Table 2. Soybean yield and production components in the crop–livestock integrated system over two years (2018 and 2019) using three different forage species as cover crops: Urochloa ruziziensis, Megathyrsus maximus cv. BRS Zuri, and Megathyrsus maximus cv. BRS Tamani.
YearPastureGrain Yield (kg ha−1)Stand
(Plants m−2)		Plant Yield
(g planta−1)
Ruziziensis3.795 a14.9 a12.8 a
2018BRS Zuri3.796 a12.9 b14.7 a
BRS Tamani3.461 a12.2 b14.3 a
Ruziziensis3.904 a10.5 a17.0 a
2019BRS Zuri4.637 a10.4 a20.9 a
BRS Tamani4.660 a9.8 a18.3 a
MeanRuziziensis3.849 a12.2 a14.9 a
BRS Zuri4.216 a11.6 ab17.8 a
BRS Tamani4.060 a11.0 b16.3 a
p-ValueForagens0.0013 **ns
Yearnsnsns
Forage × Yearnsnsns
Means followed by different letters in the same column differ significantly (p < 0.05) according to Tukey testing. ns: not significant (p > 0.05); **: means significant effect with p < 0.01. Forage × Year: means the interaction effect between forage species and year in the model.
Table
Table 3. Economic results of livestock production using three forage species in a crop–livestock integrated system over two years (2018 and 2019).
Table 3. Economic results of livestock production using three forage species in a crop–livestock integrated system over two years (2018 and 2019).
PastureCarcass Cost
(USD kg−1)		Total Cost
(USD ha−1)		Net Income
(USD ha−1)		Herd Value
(USD ha−1)		Equity
(USD ha1)		ROEm *
(%)		ROEy
(%)
2018
Ruziziensis50.21150.74 b−11.12 b1120.434007.38−0.07 b−0.83 b
BRS Zuri24.71312.61 a163.83 a944.333818.201.07 a13.66 a
BRS Tamani40.56275.81 ab94.75 a1295.484201.650.56 a6.98 a
2019
Ruziziensis12.51119.8979.84853.743532.780.566.99
BRS Zuri16.42141.2978.651033.263734.880.536.50
BRS Tamani34.28225.14110.22649.873403.780.8110.16
Mean
Ruziziensis31.06135.0735.09984.953766.270.253.08
BRS Zuri20.50225.57120.56989.513775.870.8010.08
BRS Tamani37.37250.07102.60967.503796.310.698.57
SD14.1876.6756.29211.74241.870.384.81
* Return on equity (ROE) calculated considering 4 months for the livestock phase in one year of production for the crop–livestock integrated system. SD: standard deviation. Equity: sum of total cost, herd value, and land value (USD 2725.10 ha−1).
Table
Table 4. Economic results of the production system during the two years of soybean and livestock production using three different forage cultivars.
Table 4. Economic results of the production system during the two years of soybean and livestock production using three different forage cultivars.
ForageTC
(USD ha−1)		GI
(USD ha−1)		NI
(USD ha−1)		Equity
(USD)		ROEm
(%)		ROEy
(%)
Ruziziensis2189.532802.90612.654871.580.526.47
BRS Zuri2199.043238.901039.874881.080.8911.19
BRS Tamani2283.623188.83905.224965.660.769.51
TC: total cost; GI: total gross income; NI: net income; Equity: sum of total cost, herd value, and land value (considered equal to USD 2725.10 ha−1); ROEm and ROEy: return on equity per month and per year, respectively.
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Leal, V.N.; Santos, D.d.C.; Paim, T.d.P.; Santos, L.P.d.; Alves, E.M.; Claudio, F.L.; Calgaro Junior, G.; Fernandes, P.B.; Salviano, P.A.P. Economic Results of Forage Species Choice in Crop–Livestock Integrated Systems. Agriculture 2023, 13, 637. https://doi.org/10.3390/agriculture13030637

AMA Style

Leal VN, Santos DdC, Paim TdP, Santos LPd, Alves EM, Claudio FL, Calgaro Junior G, Fernandes PB, Salviano PAP. Economic Results of Forage Species Choice in Crop–Livestock Integrated Systems. Agriculture. 2023; 13(3):637. https://doi.org/10.3390/agriculture13030637

Chicago/Turabian Style

Leal, Vanessa Nunes, Darliane de Castro Santos, Tiago do Prado Paim, Luizmar Peixoto dos Santos, Estenio Moreira Alves, Flavio Lopes Claudio, Guido Calgaro Junior, Patrick Bezerra Fernandes, and Paulo Alexandre Perdomo Salviano. 2023. "Economic Results of Forage Species Choice in Crop–Livestock Integrated Systems" Agriculture 13, no. 3: 637. https://doi.org/10.3390/agriculture13030637

APA Style

Leal, V. N., Santos, D. d. C., Paim, T. d. P., Santos, L. P. d., Alves, E. M., Claudio, F. L., Calgaro Junior, G., Fernandes, P. B., & Salviano, P. A. P. (2023). Economic Results of Forage Species Choice in Crop–Livestock Integrated Systems. Agriculture, 13(3), 637. https://doi.org/10.3390/agriculture13030637

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