Does Crop-Livestock-Forest Systems Contribute to Soil Quality in Brazilian Savannas?

Zago, Leciana Menezes Souza; Ramalho, Werther Pereira; Caramori, Samantha Salomão

doi:10.1590/2179-8087.034318

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…Soil quality in integrated crop–livestock–forestry systems is assessed by a range of soil quality indicators, including biological, physical and chemical soil properties or processes (Borges et al., 2019; Laroca et al., 2018; Zago et al., 2019). Overall, such systems are known to promote a better soil environment (Kichel et al., 2014), which is related to the synergistic effects among the components in integrated crop–livestock–forest systems (Costa et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Soil quality assessments in integrated crop–livestock–forest systems: A review

Valani

Martíni

Silva

et al. 2020

Soil Use and Management

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Integrated crop–livestock–forest is a promising strategy to improve soil quality. It comprises four different integrated farming systems: crop–livestock, crop–forest, forest–livestock and crop–livestock–forest. This work systematically reviewed studies about integrated crop–livestock–forest systems and soil quality. A total of 92 papers were retrieved from the Web of Science—Clarivate Analytics platform, and the following information was analysed: publication year, institution, region of the studied site, type of integrated system, soil type, tillage system, maximum soil depth and the soil quality indicators assessed. Most studies were published in the second half of the 2010s. Brazil is a prominent focus of research about soil quality and integrated crop–livestock–forest systems, with significant contribution from its central and southern regions. The Embrapa was the main publishing institution, present in over one‐third of the studies. Crop–livestock was the most common integrated system, Ferralsols was the most common soil group, and most of the studied soils were clayey. No tillage was the main tillage system. Most studies focused on the topsoil, assessing physical and/or chemical soil quality indicators. More emphasis on biological indicators of soil quality is required, as well as assessments integrating biological, physical and chemical indicators of soil quality. Future works should compare different integrated systems, including assessments deeper in the soil profile, especially in systems with the forest component, and also in sandy and silty soils. Soil quality indicators that have been rarely used should be further tested. Novel indicators should be added to better understand the promotion of soil quality by integrated crop–livestock–forest systems.

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Section: Introductionmentioning

confidence: 99%

Soil quality assessments in integrated crop–livestock–forest systems: A review

Valani

Martíni

Silva

et al. 2020

Soil Use and Management

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“…Systems managed with pastures are important in maintaining soil quality, given the extension of these cultivated areas across the planet (Salton et al, 2008;Santos et al, 2019), especially pastures with adequate management, which present an increase in SOM in surface and subsurface layers due to root decomposition (Coonan et al, 2019;Malcolm et al, 2019). On the other hand, pastures undergoing degradation stages, with low biomass production and soil cover and under the beginning of the emergence of erosive processes, negatively influence soil quality, compromising soil C quality and quantity (Segnini et al, 2019;Zago et al, 2019;Webster et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Soil Carbon Stock in Different Uses in the Southern Cone of Mato Grosso Do Sul

Martins

Troian

Rosset

et al. 2020

Rev. Agric. Neotrop.

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This study aimed to assess the soil density (Sd) and the total organic carbon contents and stocks in different management systems and implementation times in the municipality of Iguatemi, MS, Brazil. A completely randomized design with four replications was applied to four areas: conventional tillage (CT), reformed pasture (RP), and degraded pasture (DP), in addition to a native forest (NF) area with no anthropic action. Disturbed and undisturbed soil samples were collected at depths of 0–0.05, 0.05–0.1, and 0.1–0.2 m. The determination of Sd and total organic carbon content (TOC) allowed calculating the carbon stock (Cstock), the stratification index (SI), and the carbon stock variation (ΔCstock), with subsequent multivariate analysis. The NF area presented the highest TOC contents at all depths compared to the managed areas, with a value of 17.45 g kg−1 at a depth of 0–0.05 m. The RP and NF areas showed similar SI, which was higher in PD, with a value of 2.54. Except for RP, the other managed areas showed a negative ΔCstock in the soil profile relative to the NF area. The RP and NF areas promoted the maintenance of TOC in the soil, while DP and CT compromised the edaphic quality.

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“…Usually, fast-growing eucalypt plantations are grown in monoculture. There is, however, a recent increase in crop-livestockforest system area [6]. Eucalyptus trees established in crop-live-stock-forest systems are usually planted in rows, presenting lower stand density and diverse canopy structures.…”

Section: Introductionmentioning

confidence: 99%

High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)

Corte

Neto

Rex

et al. 2022

Drones

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Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m2), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R2, Bias, and residual charts. The basal area values ranged from 7.40 m2 ha−1 (C1380) to 8.14 m2 ha−1 281 (C2300), about −5.9% less than the real value (8.65 m2 ha−1). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m3) to the real total value (683.88 m3) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m2 is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time.

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Does Crop-Livestock-Forest Systems Contribute to Soil Quality in Brazilian Savannas?

Cited by 12 publications

References 22 publications

Soil quality assessments in integrated crop–livestock–forest systems: A review

Soil quality assessments in integrated crop–livestock–forest systems: A review

Soil Carbon Stock in Different Uses in the Southern Cone of Mato Grosso Do Sul

High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)

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