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Oil palm (OP) plantations account for 1.7 % of global CO2 emissions. Numerous studies have focused primarily on greenhouse gas (GHG) emissions from peatlands, constituting 20% of total OP area in the two largest OP producing countries, Indonesia and Malaysia. Few studies have investigated the potential for reducing GHG emissions in OP plantations. Strategies to reduce emissions and sequester carbon must consider how different practices affect production and the environment. Understanding the spatial distribution of GHG intensity and how the environment affects GHG intensity is therefore key to sustainable oil palm production.GHG intensity was used as a metric to map the potential for sustainable OP plantations. GHG intensity represents the GHG emissions / removals (ton C ha-1) per unit of oil palm yields (ton ha-1). The approach for analysing the change in GHG emissions/ removals, referred to as the IPCC tier 1 method, is based on changes in soil organic carbon due to C and N emissions in drained peatlands and the associated change in aboveground biomass due to land use change. Changes in GHG intensity were investigated spatially for a case study in an industrial OP plantation located in Riau Province, Indonesia, from 2015 to 2019. Linear regression was used to analyse the relationships between GHG intensity and agri-environmental variables including NDVI, NPP, GPP, evapotranspiration, soil moisture in the root zone, soil moisture in deeper layer, C and N emissions from organic soils, and soil organic carbon (SOC).The results show that around 90% of the new oil palm plantations in 2019 were converted from timber plantation, swamp scrubland, and bare land in 2015. Consequently, biomass growth from land use change acted as a carbon sink in this period. However, drained organic soils contributed significantly to GHG emissions. The change in GHG intensity in OP plantation in this study varied spatially from emitting (0.19 to 4.10 Ton C eq Ton-1 yields) to removing the GHG (0.23 to 2.40 Ton C eq Ton-1 yields). Among the environmental variables, NDVI and soil moisture showed the strongest relationship with GHG emissions/ removals (R2 = 0.23, p value = < 2.2e-16) and yields (R2 = 0.2 p value = < 2.2e-16) in OP plantations.These initial findings are advantageous for spatially identifying potential OP plantations that remove or emit GHG. Understanding the relationship between GHG emissions/removals and yields to environment variables provides insight into monitoring and enhancing OP sustainability, both from production and environmental perspectives. Future work will examine non-linear approaches to better model this relationship.

Localized prostate cancer (T1-3N0M0) has therapeutic options such as radical prostatectomy (RP), external beam radiation therapy (EBRT) and brachytherapy (BT). However, the evidence of the outcome of these treatments is limited and no studies have been conducted comparing biochemical failure (BF) and toxicity associated with surgical treatment and EBRT + high-dose brachytherapy (HDBT) in the region.Retrospective cohort study, clinical records of patients diagnosed with localized prostate cancer between 2014 and 2018 were reviewed at one of the main private neoplasm centers in Lima, Peru; Cox regression was used for both the BF outcome and the grade 2 toxicity outcome, calculating the hazard ratio (HR) with 95% confodence interval (CI).Of 549 patients, 76.3% (419) received RP as primary treatment, and 72% were between 50 and 70 years old at the time of diagnosis. The patients treated with EBRT + HDBT presented worse characteristics. The EBRT + HDBT group had a 40% lower risk of presenting BF (HR = 0.6; 95% CI: 0.4-0.9), and also a 50% greater risk of presenting toxicity greater than or equal to grade 2 (HR = 1.5; 95% CI: 1.0-2.0) than the group treated with RP.Our results show that when comparing patients treated with EBRT + HDBT and RP, BF was greater in RP, and post-treatment toxicity was greater in EBRT + HDBT.

Monitoring of soil water is essential to assess drought risk over rainfed agriculture. Soil water indicates the onset or progress of dry spells, the start of the rainy season and good periods for sowing or harvesting. Monitoring soil water over rainfed agriculture can be a valuable tool to support field activities and the knowledge of climate risks.A network of soil moisture sensors was established over the Brazilian North East semiarid region in 2015 with measurements at 10 and 20 cm, together with rainfall and other variables in a subset of locations. The data are currently being used to assess the available water over the region in monthly bulletins and reports of potential impacts on yields.In this work, we present a comparison of a dataset of observations from 2015 to 2019 with the soil water estimated by the JULES land surface model (the Joint UK Land Environment Simulator). Overall, the model captures the spatial and temporal variability observed in the measured data well, with an average correlation coefficient of 0.6 across the domain. The performance was compared for each station, resulting in a selection of locations with significant correlation.Based on the regression results, we derive modelled soil moisture for the time span of the JULES run (1979 to 2016). The modeled data enabled the calculation of a standardized soil moisture anomaly (SSMA). The values of SSMA in the period were in agreement with the patterns of drought in the region, especially the recent long-term drought in the Brazilian semiarid region, with significant dry years in 2012, 2013 and 2015. Further analysis will focus on comparisons with other drought indices and measures of impacts on yields at the municipality level.