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Tillage conservation practices (CA), traditional agriculture (TA), and liming influence soil properties and crop yield. However, it is essential to demonstrate which tillage and liming practices improve soil properties and forage corn yield. This study compared soil properties and forage corn production in two tillage systems with the addition of dolomite and lime, which formed four treatments. The tillage in the first three days surpassed the TA soil CO2 emission, with 64.8% more CO2 than in the CA soil, and the TA hydraulic conductivity and bulk density were more suitable than those in the CA soil. The CA soil had 233 earthworms m−2 more than in TA. The TA green forage corn yielded 6.45 t ha−1 more than in CA, with a higher P, Ca, and Mg foliar content than in CA, but in the CA, the foliar N and K were higher than in TA. The liming increased soil cations (except K), highlighting the lime on dolomite with—52% Al and + 4.85 t ha−1 of forage corn compared to the control. Soil CO2 emission was far lower in CA than in TA, with a slightly lower forage yield, and other soil properties were improved, meaning lower land preparation costs and time savings than in TA. Lime improved acidic soil faster than dolomite, generating higher forage yields. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions. [ABSTRACT FROM AUTHOR]

The domestic South American camelid Vicugna pacos L. is distributed along Peru, Chile, Bolivia, and Argentina. Here, we contribute to the bioinformatics and evolutionary systematics of the Camelidae by performing high-throughput sequencing analysis on the black Huacaya breed of V. pacos from Puno, Peru. The black Huacaya breed mitogenome is 16,664 base pairs (bp) in length and contains 37 genes (GenBank accession MT044302). The mitogenome shares a high-level of gene synteny to other Camelidae (Camelops, Camelus, Lama, and Vicugna). The mitogenome of the black Huacaya breed of V. pacos situates it in a clade with V. vicugna Molina, sister to Lama. We anticipate that further mitogenome sequencing of different breeds from Vicugna pacos will improve our understanding of the evolutionary history of this taxon. [ABSTRACT FROM AUTHOR]