Search Results (30)

The objective of this study was to evaluate the physical-hydric properties of a Planosol under an Integrated Crop–Livestock–Forest (ICLF) system in the Agreste region of Paraíba, Brazil, after eight years of implementation, and to compare them with areas under a conventional cropping system and secondary native vegetation. The experiment was conducted at the experimental station located in Alagoinha, in the Agreste mesoregion of the State of Paraíba, Brazil. The experimental design adopted was a randomized block design (RBD) with five treatments and four replications (5 × 4 + 2). The treatments consisted of: (1) Gliricidia (Gliricidia sepium (Jacq.) Steud) + Signal grass (Urochloa decumbens) (GL+SG); (2) Sabiá (Mimosa caesalpiniaefolia Benth) + Signal grass (SB+SG); (3) Purple Ipê (Handroanthus avellanedae (Lorentz ex Griseb.) Mattos) + SG (I+SG); (4) annual crop + SG (C+SG); and (5) Signal grass (SG). Two additional treatments were included for statistical comparison: a conventional cropping system (CC) and a secondary native vegetation area (NV), both located near the experimental site. The CC treatment showed the lowest bulk density (1.23 g cm⁻³) and the lowest degree of compaction (66.3%) among the evaluated treatments, as well as a total porosity (TP) higher than 75% (0.75 m³ m⁻³). In the soil under the integration system, the lowest bulk density (1.38 g cm⁻³) and the highest total porosity (0.48 m³ m⁻³) were observed in the SG treatment at the 0.0–0.10 m depth. High S-index values (>0.035) and a low relative field capacity (RFc < 0.50) and K_θ indicate high structural quality and low soil water storage capacity. It was concluded that the SG, I+SG, SB+SG, and CC treatments presented the highest values of soil bulk and degree of compaction in the layers below 0.10 m. The I+SG and C+SG treatments showed the lowest hydraulic conductivities and macroaggregation. The SG and C+SG treatments had the lowest available water content and available water capacity across the three analyzed soil layers. Full article

Tropical pasture degradation represents a major challenge for global food security and environmental conservation, particularly in Brazil, where up to 60% of pastures are degraded. This study evaluates the economic viability of recovery of degraded pastures using an integrated crop–livestock–forest (ICLF) system. A representative 2-hectare system in the Brazilian Cerrado was analyzed, featuring native Dipteryx alata trees interplanted with pasture for cattle grazing. A deterministic financial model was developed to simulate annual cash flows over a 20-year period under various financing scenarios, including self-financing and multiple subsidized rural credit lines (e.g., Pronaf and Pronamp programs, and ABC Ambiental). The analysis shows that subsidized credit lines with low interest rates and extended grace periods significantly improve project profitability, yielding positive NPVs and robust internal rates of return, while self-financing and high-cost credit options (such as Pronaf Mulher) result in negative NPVs. The dual cash flow strategy—where borrowed funds are immediately invested in secure fixed-income instruments—further enhances economic performance. The findings demonstrate that ICLF-based pasture recovery is economically viable when supported by appropriate financing, offering a scalable model for sustainable agriculture that delivers both economic and environmental benefits. Full article

Silage maize is vital to livestock development in northern China, but intensive chemical fertilization has led to soil degradation and reduced productivity. Bio-organic fertilizers offer a sustainable alternative, though their effects on soil multifunctionality remain underexplored. This study evaluated the impact of combining decomposed cow manure, Bacillus amyloliquefaciens, and mineral potassium fulvic acid with chemical fertilizers (NPK) on silage maize yield, soil microbial diversity, and ecosystem multifunctionality (EMF). Field experiments showed that bio-organic fertilization increased silage maize yield by 10.23% compared to chemical fertilizers alone, primarily by boosting labile organic carbon and soil enzyme activity. It also enhanced bacterial richness and diversity, with little effect on fungal communities. Microbial network analysis revealed more complex and stable bacterial networks under bio-organic treatments, indicating strengthened microbial interactions. Random forest and structural equation modeling (SEM) identified soil carbon storage and bacterial diversity as key drivers of EMF, which integrates soil functions such as nutrient cycling, decomposition, enzyme activity, and microbial diversity. These findings suggest that soil bacterial diversity and its interactions with soil properties are critical to both crop productivity and soil health. The optimal fertilization strategy for silage maize in this region involves the combined use of cattle manure, Bacillus amyloliquefaciens, mineral potassium fulvic acid, and NPK fertilizers. This approach improves yield, microbial diversity, and soil multifunctionality. Future studies should consider environmental variables and crop varieties across diverse regions to support broader application. Full article

Sustainable agriculture relies on effective soil management, making it crucial to assess soil health, especially in areas of agricultural expansion, such as the Cerrado in the MATOPIBA region. Sustainable strategies, such as integrated production systems (crop–livestock–forestry), are essential to mitigate these impacts. However, little is known about the effects of these systems on soil microbial communities. The objective of this study was to evaluate bacterial communities associated with soils under different integrated production systems in the MATOPIBA region. Soil samples from the 0–10 cm depth layer were collected from the following land use systems: (i) native Cerrado vegetation (NCV), (ii) native Babassu forest (NPV), (iii) no-tillage soybean—regional standard system (NT-S), (iv) crop–forest integration (CFI), (v) crop–livestock integration (CLI), and (vi) livestock–forest integration (LFI). We measured chemical properties and bacterial communities using next-generation sequencing (NGS) of the V3-V4 hypervariable region of the 16S rRNA gene. The results revealed that the integration systems (CFI, CLI, and LFI) resulted in changes in soil chemical properties, which contributed to the modulation of the bacterial communities. The most abundant taxa in integrated production systems shows a positive correlation with soil pH and phosphorus content. Members of the Nitrosomonadaceae and Sphingomonadaceae families are more related to integrated production systems containing a forestry component (CFI and LFI), while Bacillaceae are more evident in crop–livestock integration systems (CLI). Full article

Conservation systems involving trees enhance the sustainability of tropical soils. However, little is known on the effect of integrated systems with native and exotic trees on soil chemical quality in the eastern Amazon. We aimed to measure changes in soil chemical quality in integrated production systems in Pindaré-Mirim, Maranhão, Brazil. This study was carried out in 2017 and 2018, evaluating (i) perennial pasture; (ii) crop–livestock–forest integration-I (CLFI-I)—eucalyptus rows interspersed with maize + Urochloa brizantha intercropping; (iii) CLFI-II—babassu palm trees (Attalea speciosa Mart.) with maize + Megathyrsus maximus intercropping; and (iv) maize + M. maximus intercropping. Soil chemical attributes at depths of 0.00–0.10 m, 0.10–0.20 m, 0.20–0.30 m, and 0.30–0.50 m, forage productivity, and soil cover were evaluated. CLFI-II promoted the highest soil organic matter concentration in topsoil and highest pH, lowest Al³⁺ levels, and potential acidity (H+Al) at all soil depths. Soil under pasture showed the highest N, K⁺, Ca²⁺ concentrations, sum of bases, and cation exchange capacity. Changes in CLFI-II are associated with the babassu palm’s ability to modulate the surrounding environment, giving the species a competitive advantage in anthropic environments. The time of adoption is crucial for improving soil fertility in the Brazilian eastern Amazon. Sustainable production systems in the region must comply with long-term management plans. Full article

Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS management, mapping them is a challenging task. The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes. Targeting ICLS, field data were combined with S2 and PS data to build land use and land cover classification models for three sequential agricultural years (2018/2019, 2019/2020, and 2020/2021). We tested three experimental settings to assess the classification performance using S2, PS, and DF data cubes. The test classification algorithms included Random Forest (RF), Temporal Convolutional Neural Network (TempCNN), Residual Network (ResNet), and a Lightweight Temporal Attention Encoder (L-TAE), with the latter incorporating an attention-based model, fusing S2 and PS within the temporal encoders. Experimental results did not show statistically significant differences between the three data sources for both study areas. Nevertheless, the TempCNN outperformed the other classifiers with an overall accuracy above 90% and an F1-Score of 86.6% for the ICLS class. By selecting the best models, we generated annual ICLS maps, including their surrounding landscapes. This study demonstrated the potential of deep learning algorithms and SITS to successfully map dynamic agricultural systems. Full article

Livestock systems have been identified as major emitters of greenhouse gases due to the use of extensive areas with degraded pastures. The objective of this study was to analyze carbon (CO₂) and methane (CH₄) fluxes in the atmosphere as indicators of environmental sustainability in silvopastoral systems. CO₂ and CH₄ fluxes from soil to the atmosphere were monitored in a degraded pasture (predominant species: Panicum maximum cv. Mombaça) grown in full sun and compared with areas with tree species (Bertholletia excelsa, Dipteryx odorata, and Khaya grandifoliola) and productive pasture (Panicum maximum cv. Mombaça) grown in full sun. The study area was in Mojuí dos Campos, western Pará state, Eastern Amazon, Brazil. The evaluations were conducted in a Technological Reference Unit with a silvopastoral system, where animals used the shade of trees during high-temperature periods. The fluxes were measured using an ultraportable greenhouse gas analyzer coupled with static polyvinyl chloride ring chambers installed at the soil–air interface. In conclusion, areas with integrated systems (B. excelsa + pasture and K. grandifoliola + pasture) were better mitigators of CO₂ emissions; the highest emissions occurred in the degraded pasture area during the rainiest months. The CH₄ fluxes were more intense in the areas with degraded pasture and K. grandifoliola + pasture. Converting degraded pasture areas into integrated crop–livestock–forest systems reduced greenhouse gas emissions in the Amazon over 10 years of implementation. The implementation of integrated crop–livestock–forest systems in long-deforested areas with degraded pastures and a low production capacity showed high potential for changes focused on developing sustainable agriculture in the Amazon. Full article

The behavior of ruminants can influence their productive efficiency. The aim of this study was to evaluate the behavior of young zebu and composite bulls kept in pasture production systems, either in a crop-livestock-forest integration or without afforestation. The work was carried out in São Carlos, Brazil (21°57′42″ S, 47°50′28″ W), in a high-altitude tropical climate, from March to July, 2022. Forty young bulls were evaluated, being 20 Nelore (Bos indicus) (342.5 ± 36.6 kg BW; 16.9 ± 1.8 months) and 20 Canchim (5/8 Bos taurus × 3/8 Bos indicus) (338.4 ± 39.8 kg BW; 19.1 ± 1.9 months), equally distributed in full-sun (FS) and integrated crop–livestock–forestry (ICLF) production systems. Behavior was monitored uninterruptedly by an acoustic sensor and accelerometer attached to a collar, and complemented by direct visual assessment, in two one-day campaigns per month. Serum cortisol concentration was assessed monthly. Statistical analyses were conducted using a general linear model at a 5% significance level (SAS, version 9.4). The ICLF system had a milder microclimate and favored thermal comfort. Natural shading influenced grazing, resting, and rumination time. The Canchim bulls were more active when moving and grazing (p < 0.05), even at the hottest times of the day. In turn, the Nelore bulls spent more time resting at all times (p < 0.001), which was shown to be an adaptive strategy in response to environmental stimuli. The Canchim bulls had a longer rumination time than the Nelore bulls (p < 0.001), due to their longer grazing time. The frequency of water and mineral mixture intake did not differ between genotypes, regardless of the production system (p > 0.05). There was no difference in the serum cortisol concentrations of the Nelore and Canchim bulls kept in FS or ICLF (p = 0.082). Thus, young bulls of the different genotypes showed different behaviors, regardless of whether they were kept on pasture without afforestation or in an integrated crop–livestock–forestry system. Full article

Global food security and nutrition is suffering from unprecedented challenges. To reach a world without hunger and malnutrition by implementing precision agriculture, satellite remote sensing plays an increasingly important role in field crop monitoring and management. Alfalfa, a global widely distributed forage crop, requires more attention to predict its yield and quality traits from satellite data since it supports the livestock industry. Meanwhile, there are some key issues that remain unknown regarding alfalfa remote sensing from optical and synthetic aperture radar (SAR) data. Using Sentinel-1 and Sentinel-2 satellite data, this study developed, compared, and further integrated new optical- and SAR-based satellite models for improving alfalfa yield and quality traits prediction, i.e., crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and neutral detergent fiber digestibility (NDFD). Meanwhile, to better understand the physical mechanism of alfalfa optical remote sensing, a unified hybrid leaf area index (LAI) retrieval scheme was developed by coupling the PROSAIL radiative transfer model, spectral response function of the desired optical satellite, and a random forest (RF) model, denoted as a scalable optical satellite-based LAI retrieval framework. Compared to optical vegetation indices (VIs) that only capture canopy information, the results indicate that LAI had the highest correlation (r = 0.701) with alfalfa yield due to its capacity in delivering the vegetation structure characteristics. For alfalfa quality traits, optical chlorophyll VIs presented higher correlations than LAI. On the other hand, LAI did not provide a significant additional contribution for predicting alfalfa parameters in the RF developed optical prediction model using VIs as inputs. In addition, the optical-based model outperformed the SAR-based model for predicting alfalfa yield, CP, and NDFD, while the SAR-based model showed better performance for predicting ADF and NDF. The integration of optical and SAR data contributed to higher accuracy than either optical or SAR data separately. Compared to a traditional embedded integration approach, the combination of multisource heterogeneous optical and SAR satellites was optimized by multiple linear regression (yield: R² = 0.846 and RMSE = 0.0354 kg/m²; CP: R² = 0.636 and RMSE = 1.57%; ADF: R² = 0.559 and RMSE = 1.926%; NDF: R² = 0.58 and RMSE = 2.097%; NDFD: R² = 0.679 and RMSE = 2.426%). Overall, this study provides new insights into forage crop yield prediction for large-scale fields using multisource heterogeneous satellites. Full article

Sustainable land management (SLM) practices are often touted as a vehicle for simultaneously increasing agricultural productivity and food security in rural areas. In Eastern Zambia, numerous initiatives such as the Zambia Integrated Forest Landscape Project (ZIFLP) have been implemented. Yet, empirical data suggest relatively low levels of SLM uptake in the smallholder farming sector. Therefore, the broad objective of this study was to estimate the relationship between smallholder farmer awareness of SLM technologies and land allocated to SLM at the farm level. We hypothesized the following: H1: Increased farmer awareness of SLM practices leads to more land allocated to SLM activities in Zambia’s Eastern Province; and H2: Adoption of specific SLM practices influences the extent of land allocated to SLM. Using an intra-household cross-sectional survey, data were collected from 761 randomly selected households from 11 chiefdoms of the Eastern Province. The Heckman selection procedure was used to analyze the study’s overarching hypothesis. Findings showed that farmers were generally conversant with SLM as a construct (>90%), with choices being influenced by gender. Conservation agriculture in the form of crop rotations, use of manure, mixed cropping, tree planting, and minimum tillage methods were the most commonly known SLM technologies among farmers. Findings also indicated that awareness is an important antecedent in the use of SLM practices (χ² = 76.6, p = 0.00), with greater access to extension being positively associated with farmer awareness (p < 0.05). The land allotted to SLM hinged on crop diversity, ownership of different types of livestock, and access to agricultural extension. These findings suggest that long-term commitments to training farmers in SLM is critical. This will be achieved when there is coherence in the information on SLM being given to farmers by all the actors working in the region. Full article

Gliricidia (Gliricidia sepium) is a tree legume that has great potential for use in agriculture because of its multiple-use characteristics. However, there is little information in the literature about the effect of agrisilvicultural systems on nitrogen (N) cycling. This study evaluated the effect of densities of gliricidia on N cycling under an agrisilvicultural system. The treatments were composed of different densities of gliricidia: 667, 1000 and 1333 plants ha⁻¹, with a fixed spacing of 5 m between the alleys. The efficiency of N use was investigated by using the ¹⁵N isotope tracer. In each plot, a transect perpendicular to the tree rows was established in two positions: (i) in the corn (Zea mays) row adjacent to the trees, and (ii) in the corn row in the center of the alley. The N fertilizer recovery efficiency ranged from 39% in the density of 667 plants ha⁻¹ to 89% with 1000 plants ha⁻¹. The effect of gliricidia on the N uptake by corn was higher in the central position of the alley with 1000 plants ha⁻¹. The agrisilvicultural system with 1000 plants ha⁻¹ was highly efficient in the recovery of mineral N, representing an excellent option for integrated production systems in tropical regions. Full article

Since the dawn of Brazilian trade, extensive cattle farming has predominated. Brazil’s extensive pasture-based system uses pasture plants adapted to climate and soil conditions with limited use of purchased inputs. However, new technologies such as integrated crop and livestock systems have recently been adopted, with government support and public policies that are intended to encourage increased agricultural production in Brazil. Domestic and international stakeholders have prioritized sustainable agricultural development in Brazil’s beef sector to reduce deforestation and other natural-habitat conversions. This review provides an overview of beef production in Brazil, focusing particularly on (1) historical factors that have encouraged an extensive, low-intensity style of production and (2) how national public policies supporting agriculture have improved sustainability in Brazil’s beef industry. Since the beginning of the twenty-first century, specific public policies for rural areas began to implement changes that addressed environmental concerns. Programs aimed at protecting secondary forests and increasing their areas are needed to offset the 42% of Brazil’s greenhouse gas emissions that come from land-use change. To produce more beef with less environmental impact, cattle ranchers need to use their land more productively. Thus, public policy initiatives need to combat deforestation and preserve the environment and local communities, while sustainably intensifying Brazil’s beef production. Full article

Regenerative agricultural practices are a suitable path to feed the global population. Integrated Crop–livestock systems (ICLSs) are key approaches once the area provides animal and crop production resources. In Brazil, the expectation is to increase the area of ICLS fields by 5 million hectares in the next five years. However, few methods have been tested regarding spatial and temporal scales to map and monitor ICLS fields, and none of these methods use SAR data. Therefore, in this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested. As a result, we found that all proposed algorithms and sensors could correctly map both study sites. For Study Site 1(SS1), we obtained an overall accuracy of 98% using the random forest classifier. For Study Site 2, we obtained an overall accuracy of 99% using the long short-term memory net and the random forest. Further, the early-season experiments were successful for both study sites (with an accuracy higher than 90% for all time windows), and no significant difference in accuracy was found among them. Thus, this study found that it is possible to map ICLSs in the early-season and in different latitudes by using diverse algorithms and sensors. Full article

Forests remain an important resource in Iran, as most of the livelihood activities of local communities, especially in the semi-arid environment of the Zagros forests, are dependent on forest resources. The aim of this study was to identify the type and extent of forest dependency. Semi-structured interviews and questionnaires were used to collect data from 170 households in Central Zagros. Results show that using firewood for fuel and non-fuel uses, harvesting edible and medicinal plants, agriculture and horticulture, and livestock grazing were the main forest livelihood activities undertaken by the households in the study area. On average, each household harvested 18.08 cubic meters of oak per year for water heating (bathing), baking bread, heating, cooking, heating milk and buttermilk, agricultural tools, house building, warehouses and shelters, fencing, branches for livestock, charcoal and harvesting firewood for sale. Of rural households, 72% used edible plants, and 86% used medicinal plants. Age, job, residence status, number of livestock, crop farming and household size were found to be correlated with forest dependency. Findings from this study contribute broadly to an integrated understanding of the bio-human dimensions of forest ecosystems, with specific reference to the study area. Full article

The recent advances in unmanned aerial vehicle (UAV)-based remote sensing systems have broadened the remote sensing applications for agriculture. Despite the great possibilities of using UAVs to monitor agricultural fields, specific problems related to missing parts in UAV orthomosaics due to drone flight restrictions are common in agricultural monitoring, especially in large areas. In this study, we propose a methodological framework to impute missing parts of UAV orthomosaics using PlanetScope (PS) and Sentinel-2 (S2) data and the random forest (RF) algorithm of an integrated crop–livestock system (ICLS) covered by grass at the time. We validated the proposed framework by simulating and imputing artificial missing parts in a UAV orthomosaic and then comparing the original data with the model predictions. Spectral bands and the normalized difference vegetation index (NDVI) derived from PS, as well as S2 images (separately and combined), were used as predictor variables of the UAV spectral bands and NDVI in developing the RF-based imputation models. The proposed framework produces highly accurate results (RMSE = 6.77–17.33%) with a computationally efficient and robust machine-learning algorithm that leverages the wealth of empirical information present in optical satellite imagery (PS and S2) to impute up to 50% of missing parts in a UAV orthomosaic. Full article