Coordinated evaporative demand and precipitation maximize rainfed maize and soybean crop yields in the USA

To understand how climate change affects crop yields, we need to identify the climatic indices that best predict yields. Grain yields are most often predicted using precipitation and temperature in statistical models, assuming linear dependences. However, soil water availability is more influential for plant growth than precipitation and temperature, and there is ecophysiological evidence of intermediate yield maximizing conditions. Using rainfed maize and soybean yields for 1970-2010 across the USA, we tested whether the aridity index, that is, the ratio of precipitation and potential evapotranspiration seasonal totals and a proxy of soil water availability, better predicts yield than growing season precipitation total, average temperature and their interaction. We also tested for non-monotonic responses allowing for intermediate yield-maximizing conditions. The aridity index alone explained 77% and 72% of maize and soybean yield variability, compared with 78% and 73% explained by temperature, precipitation and their interaction. Yield responses were non-monotonic, with yields maximized at intermediate precipitation and temperature as well as at intermediate aridity index of 0.79 for maize and 0.98 for soybean. The yield maximizing precipitation also increased with growing season average temperature, faster in maize than soybean. The intermediate yield maximizing conditions show that rainfed maize and soybean yields could both increase and decrease depending on whether climatic conditions come closer to or deviate from the yield maximizing conditions in the future. In most counties, during 1970-2010, the precipitation and aridity index were lower and temperature higher compared with those maximizing yields, suggesting that climate change will reduce yields

Combined heat and drought suppress rainfed maize and soybean yields and modify irrigation benefits in the USA

Heat and water stress can drastically reduce crop yields, particularly when they co-occur, but their combined effects and the mitigating potential of irrigation have not been simultaneously assessed at the regional scale. We quantified the combined effects of temperature and precipitation on county-level maize and soybean yields from irrigated and rainfed cropping in the USA in 1970-2010, and estimated the yield changes due to expected future changes in temperature and precipitation. We hypothesized that yield reductions would be induced jointly by water and heat stress during the growing season, caused by low total precipitation (P-GS) and high mean temperatures (T-GS) over the whole growing season, or by many consecutive dry days (CDDGS) and high mean temperature during such dry spells (T-CDD) within the season. Whole growing season (T-GS, P-GS) and intra-seasonal climatic indices (T-CDD, CDDGS) had comparable explanatory power. Rainfed maize and soybean yielded least under warm and dry conditions over the season, and with longer dry spells and higher dry spell temperature. Yields were lost faster by warming under dry conditions, and by lengthening dry spells under warm conditions. For whole season climatic indices, maize yield loss per degree increase in temperature was larger in wet compared with dry conditions, and the benefit of increased precipitation greater under cooler conditions. The reverse was true for soybean. An increase of 2 degrees C in T-GS and no change in precipitation gave a predicted mean yield reduction across counties of 15.2% for maize and 27.6% for soybean. Irrigation alleviated both water and heat stresses, in maize even reverting the response to changes in temperature, but dependencies on temperature and precipitation remained. We provide carefully parameterized statistical models including interaction terms between temperature and precipitation to improve predictions of climate change effects on crop yield and context-dependent benefits of irrigation

A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies

The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination () is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days)

Leaf- to field-level compound effects of warm and dry conditions on crops and potential mitigating strategies

Ongoing climate change has been threatening global food security. Under climate change, increasing risk of hot and dry conditions (termed compound events) is projected in many agricultural regions. Compound events cause detrimental effects on crops, yet their effects have rarely been quantified based on modeling approach. In this thesis, we established mechanistic and statistical models to analyze crop canopy temperature, transpiration rate, and yield responses to compound effects. We aimed to explore the compound effects on crops and help identifying adaptation strategies. Our results suggested that hot and dry conditions interacted in enhancing canopy temperature, i.e. the risk of potential crop heat stress, and crop yield losses. Both canopy temperature and yield losses increased from wet-cool conditions to dryhot conditions. Short-term intra-seasonal conditions and growing season averages were equally important in assessing crop responses to compound events. More intermittent precipitation regimes and longer dry spells negatively affected canopy temperature and yields even when the mean climatic conditions remained unaltered. Rainfed crop yields showed yield maximizing precipitation, which increased with temperature. As one of the adaptation strategies, irrigation could alleviate but not cancel the negative effects of adverse climate. Another adaptation is a shift from annual to perennial grain crops. Whether perennial grain crops are less vulnerable to heat and water stress depends on some key plant traits, such as leaf area index, which should be targeted for future breeding program to adapt to climate change

Canopy temperature and heat stress are increased by compound high air temperature and water stress and reduced by irrigation - a modeling analysis

Crop yield is reduced by heat and water stress and even more when these conditions co-occur. Yet, compound effects of air temperature and water availability on crop heat stress are poorly quantified. Existing crop models, by relying at least partially on empirical functions, cannot account for the feedbacks of plant traits and response to heat and water stress on canopy temperature. We developed a fully mechanistic model, coupling crop energy and water balances, to determine canopy temperature as a function of plant traits, stochastic environmental conditions, and irrigation applications. While general, the model was parameterized for wheat. Canopy temperature largely followed air temperature under well-watered conditions. But, when soil water potential was more negative than -0.14 MPa, further reductions in soil water availability led to a rapid rise in canopy temperature - up to 10 degrees C warmer than air at soil water potential of -0.62 MPa. More intermittent precipitation led to higher canopy temperatures and longer periods of potentially damaging crop canopy temperatures. Irrigation applications aimed at keeping crops under well-watered conditions could reduce canopy temperature but in most cases were unable to maintain it below the threshold temperature for potential heat damage; the benefits of irrigation in terms of reduction of canopy temperature decreased as average air temperature increased. Hence, irrigation is only a partial solution to adapt to warmer and drier climates

Validation of Erlotinib in Human Plasma

pharmaceutic

BERT-ERC: Fine-Tuning BERT Is Enough for Emotion Recognition in Conversation

Previous works on emotion recognition in conversation (ERC) follow a two-step paradigm, which can be summarized as first producing context-independent features via fine-tuning pretrained language models (PLMs) and then analyzing contextual information and dialogue structure information among the extracted features. However, we discover that this paradigm has several limitations. Accordingly, we propose a novel paradigm, i.e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy. Furthermore, we develop our model BERT-ERC according to the proposed paradigm, which improves ERC performance in three aspects, namely suggestive text, fine-grained classification module, and two-stage training. Compared to existing methods, BERT-ERC achieves substantial improvement on four datasets, indicating its effectiveness and generalization capability. Besides, we also set up the limited resources scenario and the online prediction scenario to approximate real-world scenarios. Extensive experiments demonstrate that the proposed paradigm significantly outperforms the previous one and can be adapted to various scenes

Molecular characteristics and pathogenicity of a novel chicken astrovirus variant

Abstract It is well-established that the genetic diversity, regional prevalence, and broad host range of astroviruses significantly impact the poultry industry. In July 2022, a small-scale commercial broiler farm in China reported cases of growth retardation and a 3% mortality rate. From chickens displaying proventriculitis and pancreatitis, three chicken astroviruses (CAstV) isolates were obtained and named SDAU2022-1-3. Complete genomic sequencing and analysis revealed the unique characteristics of these isolates from known CAstV strains in ORF1a, ORF1b, and ORF2 genes, characterized by an unusually high variability. Analysis of amino acid mutations in ORF1a, ORF1b, and ORF2 indicated that the accumulation of these mutations played a pivotal role in the emergence of the variant strain. Inoculation experiments demonstrated that affected chickens exhibited liver and kidney enlargement, localized proventricular hemorrhage, and a dark reddish-brown appearance in about two-thirds of the pancreas. Histopathological examination unveiled hepatic lymphocytic infiltration, renal tubular epithelial cell swelling, along with lymphocytic proventriculitis and pancreatitis. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis indicated viremia and viral shedding at 3 days post-infection (dpi). The proventriculus displayed the highest viral loads, followed by the liver, kidney, duodenum, and pancreas. Liver parameters (AST and ALT) and kidney parameters (UA and UN) demonstrated mild damage consistent with earlier findings. While the possibility of new mutations in the ORF2 gene of CAstV causing proventriculitis and pancreatitis warrants further investigation, these findings deepen our comprehension of CAstV’s pathogenicity in chickens. Additionally, they serve as valuable references for subsequent research endeavors