No abstract

Rhizobia and associative bacteria promote growth in rice plants (Oryza sativa L.) through a series of mechanisms, but most studies on inoculation have been performed based on inoculation with these bacteria in a separate or singular manner. The objective of this study was to assess the efficiency of single/isolated inoculation and inoculation combined with symbiotic rhizobia from forage legume and with Azospirillum brasilense on promoting growth and the root colonization process in wetland rice. Two rhizobia among four isolates from a greenhouse and a laboratory experiment were selected that efficiently promoted seed germination and rice plant growth in a sterilized substrate and in soil. The two most efficient isolates (UFRGS Vp16 and UFRGS Lc348) were inoculated alone or in combination with a commercial product containing A. brasilense in two field experiments using two wetland rice cultivars over two growing seasons. In the field experiments, these isolates coinoculated with A. brasilense promoted larger increases in the agronomic variables of wetland rice compared to the control without inoculation. Confocal laser microscopy confirmed the presence of inoculated bacteria tagged with gfp (UFRGS Vp16, UFRGS Lc348, and A. brasilense) colonizing the root surface of the rice seedlings, mainly in the root hairs and lateral roots.

ACKNOWLEDGEMENTS. We thank the Department of Biotechnology, New Delhi for financial support and the Department of Bioscience and Biotechnology, Banasthali University for providing the necessary facilities to carry out this work.

The recommendations of nitrogen (N) fertilization in garlic are still based on different varieties of the current types that are infected with phytopathogenic virus. There are several methods for recommendation of nitrogen (N) fertilization in garlic, but there are no enough methods for N diagnosis in garlic obtained by meristem culture. The objective of this work was to evaluate methods for diagnosing the nutritional status of virus-free garlic subjected to N doses through the use of a specific NO 3 meter in soil solution and foliar sap, portable chlorophyll meter, N content in the leaf, and its relationship with yield and quality of the bulbs. The experiments were conducted with the use of virus-free seed bulbs from the meristem culture from three sites in the 2015 growing season and two locations in the 2016 growing season in South Brazil. The treatments consisted of the application of five nitrogen doses (0, 100, 200, 300, and 400 kg ha-1) distributed in three applications during the crop cycle: 1/3 in planting, 1/3 between 30 and 40 days after planting, and 1/3 after visual bulb differentiation. The highest commercial yield was associated with doses between 269 and 307 kg ha-1 of N and the content of 26 g kg-1 of N, in the diagnostic leaf. The relative chlorophyll content was the only diagnostic technology that showed a significant correlation with commercial yield in all experimental conditions. The evaluation of the N status in the virus-free garlic crop by a portable chlorophyll meter can be a quick strategy for recommending N fertilization and ensuring high yields.

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.