Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM). We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 × 5 km pixel resolution) were obtained from ISRIC – World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ∼ 1 and ∼ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ∼ 39 % of SOC variability from repeated 5-fold cross-validation. We report a combined SOC stock of 77.8 ± 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 ± 16.5 Pg) and croplands (13 ± 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 ± 42.2 and 76.8 ± 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates.

The use of yeasts for the production of proteases has increased in demand in recent years. Y. lipolytica has been reported as a strain with high yields of protease production. This work aimed to evaluate the impact of pH and temperature on the production of proteases using Y. lipolytica in solid-state fermentation (SSF). Soybean, canola meal, cottonseed meal, and sesame meal wastes were used as nutrient sources at seven pH levels (4, 5, 6, 7, 8, 9, 10) and five temperatures (25, 30, 35, 40, 45 °C). The waste source and optimal conditions for maximum enzyme production (EP) were obtained by Box–Benhken design. The results revealed that at pH of 7, temperature of 30 °C, and for 48 h cultivation period, canola meal showed the best EP with 188.75 U/L, followed by soybean with 117.07 U/L, cottonseed meal with 66.71 U/L, and sesame with the lowest production, reaching 88.5 U/L up to 35 °C. The temperature factor exhibited the greatest effect on protease production. The biotechnological and economic potential of canola meal in the production of enzymes is highlighted.

Cultivation of C₃ and C₄ crops in semi-arid regions will be severely constrained as global temperatures rise. Consequently, alternative crops need to be sought out that adapt well to heat and drought and are productive despite limited access to water. Traits, such as crassulacean acid metabolism (CAM), enable economically important species such as those in the Agave genus adapt to drought and high temperatures. The succulence and high efficiency of agaves, which enables them to produce biomass with little water, underscores their feasibility as an alternative crop for semi-arid regions, such as the Sonoran Desert in the southwestern U.S. In this paper, we offer a review of the suitability for cultivation of agaves via dryland farming, particularly by rock mulching techniques used by pre-Columbian, Sonoran Desert farmers. This analysis dovetails with information also provided on the biological traits of Agave and its historical and present utilization. Pre-Columbian, Hohokam dryland farmers used rock mulching in the form of rock piles to cultivate agaves. Rock piles acted as a type of mulch to harvest rainfall and to retain soil moisture, which allowed the Hohokam to intensively cultivate agaves during multi-year droughts. Remains of Hohokam rock mulching for agave production can be found at archaeological sites in central Arizona, which provides evidence of the utility of dryland farming and ancient agricultural innovation to reconcile water scarcity in the region. Moreover, the use of rock piles likely bolstered Agave productivity in marginal lands. Although little is known of historic rock mulching to cultivate agaves and its biological implications on plant productivity we suggest its application as a dryland farming model could be a sustainable strategy in the U.S. Southwest.

One of the conifers that survived after the last glaciation is the Pseudotsuga menziesii (Mirb.) Franco. Due to the gradual increase in temperature, this species was forced to move from the south to the north and to higher elevation, causing a fragmented and intermittent distribution in Mexico. The main objective of this study was to model and identify suitable areas for the future conservation of the P. menziesii in Mexico. The specific objectives of this research were: (i) to model the habitat suitability of P. menziesii in Mexico, (ii) to identify the most relevant environmental variables based on its current and future habitat suitability (2030, 2050, 2070 and 2090) and (iii) to suggest areas for the conservation of the species in Mexico. Records were compiled from different national and international sources. Climate and topographic variables were used. With MaxEnt software version 3.4.3 (Phillips, New York, NY, USA) 100 distribution models were obtained, where the model showed an area under the curve of 0.905 for training and 0.906 for validation and partial ROC of 1.95 and Z reliable (p < 0.01), with TSS values > 0.80. The current area of the P. menziesii was 31,580.65 km². The most important variables in the current and future distribution were maximum temperature of the hottest month, precipitation of the coldest trimester and average temperature of the coldest trimester. The percentage of permanence (resilience) for the 2030, 2050, 2070 and 2090 climate horizons was 49.79%, 25.14%, 17.45% and 16.46%, respectively, for the SSP 245 scenario. On the other hand, for the SSP 585 scenario and the analyzed horizons, the percentage resilience in areas of suitable habitat zones was 41.45%, 27.42%, 9.82% and 2.89%.

Country-specific soil organic carbon (SOC) maps are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM). We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included: support vector machines, random forest, kernel weighted nearest neighbors, partial least squares regression, and regression-Kriging based on stepwise multiple linear models. Country-specific training data and SOC predictors (5 × 5 km pixel resolution) were obtained from ISRIC-World-Soil-Information-System. In general, temperature, soil type, vegetation indices and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific data scenarios and were able to explain ~ 53 % of SOC variability (range