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Satellite remote sensing data expedite crop yield estimation, offering valuable insights for farmers' decision making. Recent forecasting methods, particularly those utilizing machine learning algorithms like Random Forest and Artificial Neural Networks, show promise. However, challenges such as validation performances, large volume of data, and the inherent complexity and inexplicability of these models hinder their widespread adoption. This paper presents a simpler approach, employing linear regression models fitted from vegetation indices (VIs) extracted from MODIS sensor data on the Terra and Aqua satellites. The aim is to forecast cotton yields in key areas of the Brazilian Cerrado. Using data from 281 commercial production plots, models were trained (167 plots) and tested (114 plots), relating seed cotton yield to nine commonly used VIs averaged over 15-day intervals. Among the evaluated VIs, Enhanced Vegetation Index (EVI) and Triangular Vegetation Index (TVI) exhibited the lowest root mean square errors (RMSE) and the highest determination coefficients (R2). Optimal periods for in-season yield prediction fell between 90 and 105 to 135 and 150 days after sowing (DAS), corresponding to key phenological phases such as boll development, open boll, and fiber maturation, with the lowest RMSE of about 750 kg ha−1 and R2 of 0.70. The best forecasts for early crop stages were provided by models at the peaks (maximum value of the VI time series) for EVI and TVI, which occurred around 80–90 DAS. The proposed approach makes the yield predictability more inferable along the crop time series just by providing sowing dates, contour maps, and their respective VIs. [ABSTRACT FROM AUTHOR]

Meloidogyne incognita is the root-knot nematode (RKN) species globally known to infect cotton plants, and the resistant cultivar IMA 5801B2RF has recently been released in Brazil for its control. In 2019, the first infection by M. enterolobii on resistant cotton was reported in Minas Gerais state, Brazil. In 2021, in our previous survey, this nematode species was detected again in Brazil in the municipality of São Desidério, western Bahia state on the same resistant cotton cultivar. In a continuous study, we have surveyed cotton fields from six municipalities in Bahia state, in areas supposedly cultivated with the resistant cotton; all the six populations from three different geographical origins were M. incognita , but M. enterolobii was not found again, confirming its restricted occurrence in western Bahia state. The RKN identifications were based on biochemical and molecular approaches and revealed the phenotype α-esterase Est En2 for M. enterolobii and Est I2 for M. incognita and typical SCAR patterns of these species, confirming the enzymatic identification. In soil and root analysis we detected second-stage juveniles (J2) of M. incognita in 21 samples (50% of occurrence) and Rotylenchulus reniformis in 13 samples (31%) extract from soil and roots and identified using taxonomic approaches. A bioassay with the resistant cotton cultivar in greenhouse conditions revealed a high reproduction of M. enterolobii (RF = 12.8) but did not allow reproduction of the field populations of M. incognita (RF < 1.0), showing that these populations of M. incognita were not virulent to this resistant cotton. There may have been an incorrect identification in the field cultivar and/or seed mixture in on-farm seed production. Our study provided the first report of a natural infection of a new race of M. enterolobii on cotton and highlighted the absence of virulent M. incognita populations on cotton in western Bahia state, Brazil. • Cotton-pathogenic race of Meloidogyne enterolobii is present in Bahia state, Brazil. • This race of M. enterolobii is isolated in the Municipality of São Desidério. • The new cotton race M. enterolobii is virulent to cultivars with two resistance QTL. • Meloidogyne incognita is present in 50% of resistant cotton areas in Bahia state. • Any population of M. incognita from Bahia state is virulent to resistant cotton. [ABSTRACT FROM AUTHOR]