Refining phosphorus fertilizer recommendations based on buffering capacity of soils from southern Brazil

The phosphorus (P) rates recommended for corrective fertilization-P of soils from southern Brazil may be insufficient to reach the critical level for optimal plant growth. This study aimed to quantify the fertilizer-P rates for total correction fertilization with varying soil buffering capacity in the states of Rio Grande do Sul (RS) and Santa Catarina (SC). Soil samples from 0.00-0.10 and 0.10-0.20 m layers were collected from 41 locations distributed in both states. Twelve P rates were applied to each soil, varying between 0 and 100 % of the maximum adsorption capacity (P-max), and incubated for 20 days. After incubation, the extractable P was determined by Mehlich-1. Based on the relationship between applied rates and extracted P, the P buffer capacity (trP_M1) of the soils was quantified, relating it to soil properties. The trP_M1 values, that is, amounts of P 2 O 5 required to increase 1 mg dm -3 of P extracted by Mehlich-1, varied between 2.4 and 34.5 kg ha -1 of P 2 O 5 . A multiple explanatory equation for the variable P was generated, in which only P-max, clay content, and initial P availability have a significant effect. The P buffer capacity was significantly higher in the soils with the highest clay content, and there was a reduction in trP_M1 for soils with higher initial P availability. Considering 270 soil samples with low P, the P rate to reach the sufficient levels may be 2-folds higher than the values currently indicated for the RS and SC states, especially for soils with more than 40 % of clay. Phosphorus rates for corrective fertilization must be based on the soil clay content and in P initial availability. The fertilizer-P in clayey soils must be increased

Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models.

Brazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers' observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data