Higher temperatures are usually reported during meteorological drought and there are two prevailing interpretations for this observation. The first is that the increase in temperature (T) causes an increase in evaporation (E) that dries the environment. The second states that the decline in precipitation (P) during drought reduces the available water thereby decreasing E, and in turn the consequent reduction in evaporative cooling causes higher T. To test which of these interpretations is correct, we use climatic data (T, P) and a recently released database (CERES) that includes incoming and outgoing shortwave and longwave surface radiative fluxes to study meteorological drought at four sites (parts of Australia, US, and Brazil), using the Budyko approximation to calculate E. The results support the second interpretation at arid sites. The analysis also showed that increases in T due to drought have a different radiative signature from increases in T due to elevated CO 2 .
Necrotic enteritis (NE) caused by Clostridium perfringens is one of the most detrimental infectious diseases in poultry. This study examined the effect of blends of essential oils (BEOs) (25% thymol and 25% carvacrol) on NE and bacterial dynamics and functions in chicks challenged with C. perfringens. Chicks were assigned to a Control diet and BEOs diet (Control diet + 120 mg/kg BEOs), were challenged with C. perfringens from days 14 to 20 and were killed on day 21 for assessment. Supplementation with BEOs decreased the mortality, alleviated gut lesions, and decreased the virulence factors of pathogenic bacteria (VF 0073-ClpE, VF0124-LPS, and VF0350-BSH). Lack of supplementation also changed the nutrient and immunological dynamics of host microbiota in responding to C. perfringens infection. Adding BEOs changed the host ileum microbial population by increasing the numbers of Lactobacillus crispatus and Lactobacillus agilis, and decreasing Lactobacillus salivarius and Lactobacillus johnsonii. The functional roles of these changing host bacterial populations coupled with the putative reduced pathogenicity of C. perfringens by BEOs contributed to the reduction in gut lesions and mortality in infected chickens. It suggests that dietary supplementation with BEOs could significantly reduce the impact of NE caused by C. perfringens on broilers.
Timely and accurate forecasting of crop yields is crucial to food security and sustainable development in the agricultural sector. However, winter wheat yield estimation and forecasting on a regional scale still remains challenging. In this study, we established a two-branch deep learning model to predict winter wheat yield in the main producing regions of China at the county level. The first branch of the model was constructed based on the Long Short-Term Memory (LSTM) networks with inputs from meteorological and remote sensing data. Another branch was constructed using Convolution Neural Networks (CNN) to model static soil features. The model was then trained using the detrended statistical yield data during 1982 to 2015 and evaluated by leave-one-year-out-validation. The evaluation results showed a promising performance of the model with the overall R 2 and RMSE of 0.77 and 721 kg/ha, respectively. We further conducted yield prediction and uncertainty analysis based on the two-branch model and obtained the forecast accuracy in one month prior to harvest of 0.75 and 732 kg/ha. Results also showed that while yield detrending could potentially introduce higher uncertainty, it had the advantage of improving the model performance in yield prediction.
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