Abstract The high prevalence of Haemonchus contortus and its anthelmintic resistance have affected sheep production worldwide. Machine learning approaches are able to investigate the complex relationships among the factors involved in resistance. Classification trees were built to predict multidrug resistance from 36 management practices in 27 sheep flocks. Resistance to five anthelmintics was assessed using a fecal egg count reduction test (FECRT), and 20 flocks with FECRT < 80% for four or five anthelmintics were considered resistant. The data were randomly split into training (75%) and test (25%) sets, resampled 1,000 times, and the classification trees were generated for the training data. Of the 1,000 trees, 24 (2.4%) showed 100% accuracy, sensitivity, and specificity in predicting a flock as resistant or susceptible for the test data. Forage species was a split common to all 24 trees, and the most frequent trees (12/24) were split by forage species, grazing pasture area, and fecal examination. The farming system, Suffolk sheep breed, and anthelmintic choice criteria were practices highlighted in the other trees. These management practices can be used to predict the anthelmintic resistance status and guide measures for gastrointestinal nematode control in sheep flocks. worldwide 3 2 FECRT, , (FECRT) 80 75% 75 (75% 25% 25 (25% sets 1000 1 000 1,00 times 2.4% 4 (2.4% 100 accuracy sensitivity 12/24 1224 12 (12/24 area examination system breed (FECRT 8 7 (75 (25 00 1,0 2.4 (2.4 10 12/2 122 (12/2 (7 (2 0 1, 2. (2. 12/ (12/ ( (12 (1

Resumo A criação de ovinos é afetada pela alta prevalência de Haemonchus contortus e pela resistência anti-helmíntica, cujas complexas inter-relações podem ser elucidadas por técnicas de aprendizado de máquina. Árvores de classificação identificaram a importância de práticas de manejo em 27 rebanhos ovinos, para a predição da resistência anti-helmíntica múltipla. A resistência a cinco anti-helmínticos foi determinada pelo teste de redução de contagem de ovos nas fezes (TRCOF), e as 20 propriedades com eficácia inferior a 80% no TRCOF para quatro ou cinco anti-helmínticos foram consideradas resistentes. Os dados foram aleatoriamente separados em 1.000 amostragens de treinamento (75%) e teste (25%), e as árvores de classificação foram geradas para os dados de treinamento. Dessas, 24 árvores (2,4%) apresentaram acurácia, sensibilidade e especificidade de 100% para a predição do estado de resistência nos dados de teste. O tipo de forragem foi detectado em todas as árvores, enquanto a área de pastagem e a realização de exame de fezes foram observadas nas árvores mais frequentes (12/24). O sistema de produção, a raça ovina Suffolk e o critério de escolha do anti-helmíntico geraram quebras nas demais árvores. Essas práticas de manejo podem predizer o estado de resistência anti-helmíntica em rebanhos e orientar medidas, visando ao controle da verminose em ovinos. antihelmíntica, antihelmíntica anti helmíntica, helmíntica interrelações inter relações máquina 2 múltipla antihelmínticos helmínticos TRCOF, , (TRCOF) 80 resistentes 1000 1 000 1.00 75% 75 (75% 25%, 25 25% (25%) Dessas 2,4% 4 (2,4% acurácia 100 12/24. 1224 12/24 . 12 (12/24) produção antihelmíntico helmíntico medidas (TRCOF 8 00 1.0 7 (75 (25% 2,4 (2,4 10 122 12/2 (12/24 0 1. (7 (25 2, (2, 12/ (12/2 ( (2 (12/ (12 (1

ABSTRACT: Sugarcane (saccharum spp.) in Brazil is managed on the basis of “production environments”. These “production environments” are used for many purposes, such as variety allocation, application of fertilizers and definition of the planting and harvesting periods. A quality classification is essential to ensure high economic returns. However, the classification is carried out by few and, most of the time, non-representative soil samples, showing unreal local conditions of soil spatial variability and resulting in classifications that are imprecise. One of the important tools in the precision agriculture technological package is the apparent electrical conductivity (ECa) sensors that can quickly map soil spatial variability with high-resolution and at low-cost. The aim of the present work was to show that soil ECa maps are able to assist classification of the “production environments” in sugarcane fields and rapidly and accurately reflect the yield potential. Two sugarcane fields (35 and 100 ha) were mapped with an electromagnetic induction sensor to measure soil ECa and were sampled by a dense sampling grid. The results showed that the ECa technique was able to reflect mainly the spatial variability of the clay content, evidencing regions with different yield potentials, guiding soil sampling to soil classification that is both more secure and more accurate. Furthermore, ECa allowed for more precise classification, where new “production environments”, different from those previously defined by the traditional sampling methods, were revealed. Thus, sugarcane growers will be able to allocate suitable varieties and fertilize their agricultural fields in a coherent way with higher quality, guaranteeing greater sustainability and economic return on their production.