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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]

• This work analyses the Peruvian Creole goat (PCG) with genomic data for the first time. • PCG possesses high genetic variability and low inbreeding. • The Peruvian Creole goat is grouped in a single cluster. • Inter-breed structure demonstrated the PCG corresponds to a different population. Goat farming constitutes a significant source of income for farmers in northern Peru. There is currently an absence of information about the genetics of Peruvian Creole goats that would enable us to understand their origins and genetic spread. The objective of this study was to estimate the genetic diversity of Creole goats from northern Peru using SNP markers. This study involved the collection of 192 male Creole goats from three key goat production geographical departments in northern Peru. These goat samples were genotyped using the GGPGoat70k SNP panel. To explore the genetic influence of other breeds on Peruvian Creole goats, our dataset was combined with previously published SNP genotypes. External data set includes multiple breeds genotypes sampled from Argentina, Brazil, Spain, and Alpine breed from Italy, France, and Switzerland. After quality control 52,832 autosomal SNPs were used to assess genetic diversity in the Peruvian goats. For the population structure analysis of the merged data 20,513 common SNPs were used. Estimations for expected heterozygosity (H e), observed heterozygosity (H o), and inbreeding coefficient (F IS) were computed for the Peruvian groups. AMOVA, principal component analysis and ADMIXTURE were conducted to evaluate the population structure in the two data sets, Peru and merged. The results revealed a considerable genetic diversity, with H o values ranging from 0.40 to 0.41 for the Peruvian sampling groups, and inbreeding coefficient was notably low for Peruvian goat. The population structure analysis demonstrated a distinction (p < 0.05) from other breeds. These findings suggest a level of genetic differentiation of the Peruvian goat population among other breeds, although further research is needed considering samples from other Peruvian areas. We expect this study will contribute to define genetic management strategies to prevent the loss of genetic diversity in Peruvian goat populations and for upcoming advancements in this field. [ABSTRACT FROM AUTHOR]