There has recently been concern in Brazil whether biological N₂ fixation (BNF) is capable of meeting the increased N needs of newly released more productive cultivars, as well as doubts about the advantages of annual reinoculation of seeds. Forty experiments were performed over 3 yr in oxisols containing at least 103 cells of Bradyrhizobium g^-1 in the State of Paraná, southern Brazil to estimate the contributions of BNF and of N fertilizer. The experiments were performed at two sites, Londrina and Ponta Grossa, under conventional (CT) or no-tillage (NT) systems, with two cultivars [Embrapa 48 (early-maturing) or BRS 134 (medium-maturity group)]. Treatments included non-inoculated controls without or with 200 kg of N ha^-1, and inoculation without or with N fertilizer applied at sowing (30 kg of N ha^-1), or at the R2 or R4 stage (50 kg of N ha^-1). Compared with the non-inoculated control, reinoculation significantly increased the contribution of BNF estimated by the N-ureide technique (on average from 79 to 84%), grain yield (on average 127 kg ha^-1, or 4.7%) and total N in grains (on average 6.6%). The application of 200 kg of N fertilizer ha^-1 drastically decreased nodulation and the contribution of BNF (to 44%), with no further gains in yield. Application of starter N at sowing decreased nodulation and the contribution of BNF slightly and did not increase yields, while N fertilizer at R2 and R4 stages decreased the contribution of BNF (to 77%) and also yields. Estimates of volatilization of ammonia ranged from 15 to 25% of the N fertilizer applied, and no residual benefits of the N fertilizer in the winter crop were observed. The results highlight the economical and environmental benefits resulting from replacing N fertilizer with inoculation in Brazil, and reinforce the benefits of reinoculation, even in soils with high populations of Bradyrhizobium. Key words: Biological nitrogen fixation, Bradyrhizobium, inoculation, N fertilizers, ureides, soybean

Soybean production both in Brazil and globally has regularly been threatened by drought periods. The use of infrared thermography to evaluate the canopy’s temperature and its relationship with plant water status constitutes an important tool for agricultural monitoring. However, studies regarding the water status of soybean plants through unmanned aerial vehicle (UAV)-based thermal imaging are yet to be reported. Thus, the present study aimed to evaluate the water status of soybean plants submitted to different water conditions via thermal images obtained through an UAV thermal infrared camera. The field experiment was undertaken at the National Soybean Research Centre (Embrapa Soja, a branch of the Brazilian Agricultural Research Corporation) in a randomized complete block design, with four replicates. The following water conditions were evaluated: irrigated (IRR, receiving rainfall and irrigation when necessary, and with a soil water matric potential between −0.03 MPa and −0.05 MPa), non-irrigated (NIRR, receiving only rainfall), and water deficit (or drought stress) induced at the vegetative (DSV) and reproductive (DSR) stages. Water deficit was induced using rainout shelters. Soil moisture and weather data were monitored daily. Thermal images were obtained on twelve dates, half in 2016–2017 and half during the 2017–2018 crop seasons, through a thermal infrared camera (DIY-Thermocam) sensitive to temperatures ranging from – 40°C to 200°C, with 0.5 °C accuracy and 14-bit radiometric resolution. Images in RAW format (160 pixels x120 pixels) were obtained at 125 m above ground level. They were then processed and calibrated by acquiring a correction factor resulting from the effect of atmospheric attenuation. The canopy temperature was evaluated in relation to that of air temperature and through the Normalized Relative Canopy Temperature (NRCT). Atmospheric attenuation was positively correlated to flight altitude, so that image correction eliminated such an effect. The thermal behaviour of soybean plants was directly correlated to soil water availability and atmospheric vapour pressure deficit, with differences ranging from 0.2 °C to 7.2°C. Plants grown at lower soil moisture conditions had higher temperatures, which were observed under DSV and DSR conditions. However, plants submitted to DSV and then rehydrated under normal field conditions demonstrated lower temperatures than those presented by plants under IRR or NIRR. Water deficit was more damaging to yields when induced at the reproductive stage. We emphasize the potential use of UAV thermal infrared cameras for monitoring the water status of soybean plants, and for collecting non-destructive information in support of better crop management decisions and new studies.

The climate and its variability is the main risk factor for the success of soybean crop in southern Brazil. Such aspect becomes even more important under the future climate scenarios, in which global warming is expected. Based on that, the objectives of this study were to identify the impacts of raising temperatures on soybean yields in southern Brazil and how management strategies represented by changing sowing dates could be able to mitigate them. The soybean yields for the present and future scenarios were estimated by the crop simulation model CROPGRO-Soybean calibrated for southern Brazil. The simulations were done for 13 locations distributed in the states of Paraná, Santa Catarina and Rio Grande do Sul, considering climatic series of 31 years. The future climate scenarios were built based on downscaling temperature changes from ETA and PRECIS models for the periods: 2013–2043 (D25) and 2041–2071 (D55), without considering the increase in the atmospheric CO₂ concentration. The soybean potential (Y_p) and attainable (Y_a) yields and their relationship (Y_a/Y_p) were estimated for all scenarios and used to determine the climatic risk for the crop. In order to investigate how to mitigate the global warming impacts on soybean yields, different sowing dates, two earlier and two later than the present recommendation, were simulated in the crop model and resulting yields were compared. In general, raising temperature will lead soybean crop in southern Brazil to lower yields, for both Y_p and Y_a, with higher impact on Y_a, independently of the climate model used. Also, the climatic risk for the crop will increase in the future climates scenarios. The strategy of changing the sowing date showed to be feasible to reduce the impacts of raising temperatures on soybean yields, but only when it is delayed in relation to the sowing period presently recommended for this crop in the region.

Soybean grain yield has regularly been impaired by drought periods, and the future climatic scenarios for soybean production might drastically impact yields worldwide. In this context, the knowledge of soybean yield is extremely important to subsidize government and corporative decisions over technical issues. This paper aimed to predict grain yield in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least square regression (PLSR). Field experiments were undertaken at Embrapa Soja (Brazilian Agricultural Research Corporation) in the 2016/2017, 2017/2018 and 2018/2019 cropping seasons. The data collected were analyzed following a split plot model in a randomized complete block design, with four blocks. The following water conditions were distributed in the field plots: irrigated (IRR), non-irrigated (NIRR) and water deficit induced at the vegetative (WDV) and reproductive stages (WDR) using rainout shelters. Soybean genotypes with different responses to water deficit were distributed in the subplots. Soil moisture and weather data were monitored daily. A total of 7216 leaf reflectance (from 400 to 2500 nm, measured by the FieldSpec 3 Jr spectroradiometer) was collected at 24 days in the three cropping seasons. The PLSR (p ≤ 0.05) was performed to predict soybean grain yield by its leaf-based reflectance spectroscopy. The results demonstrated the highest accuracy in soybean grain yield prediction at the R5 phenological stage, corresponding to the period when grains are being formed (R² ranging from 0.731 to 0.924 and the RMSE from 334 to 403 kg ha⁻¹—7.77 to 11.33%). Analyzing the three cropping seasons into a single PLSR model at R5 stage, R² equal to 0.775, 0.730 and 0.688 were obtained at the calibration, cross-validation and external validation stages, with RMSE lower than 634 kg ha⁻¹ (13.34%). The PLSR demonstrated higher accuracy in plants submitted to water deficit both at the vegetative and reproductive periods in comparison to plants under natural rainfall or irrigation.