A Bio-Economic Crop Yield Response (BECYR) Model for Corn and Soybeans in Ontario, Canada for 1959-2013.
Ontology highlight
ABSTRACT:
SUBMITTER: Xu Q
PROVIDER: S-EPMC7181836 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
A Bio-Economic Crop Yield Response (BECYR) Model for Corn and Soybeans in Ontario, Canada for 1959-2013.
Xu Qin Q Fox Glenn G McKenney Daniel W DW Parkin Gary G Li Zhenyi Z
Scientific reports 20200424 1
This paper presents estimates of the effects of changing climate on crop yields for grain corn and soybeans in Ontario, Canada, for 1959-2013. We were able to use a database that is more comprehensive with respect to explanatory variables than some previous efforts had available. Our model includes climate variables, prices, land quality, groundwater level, CO<sub>2</sub> concentration, and a time trend. Our results indicate that trends in temperature and precipitation during our study period ha ...[more]
Similar Datasets
Project description:Encephalitis, a brain inflammation leading to severe illness and often death, is caused by >100 pathogens. To assess the incidence and trends of encephalitis in Ontario, Canada, we obtained data on 6,463 Ontario encephalitis hospitalizations from the hospital Discharge Abstract Database for April 2002-December 2013 and analyzed these data using multiple negative binomial regression. The estimated crude incidence of all-cause encephalitis in Ontario was ≈4.3 cases/100,000 persons/year. Incidence rates for infants <1 year of age and adults >65 years were 3.9 and 3.0 times that of adults 20-44 years of age, respectively. Incidence peaks during August-September in 2002 and 2012 resulted primarily from encephalitis of unknown cause and viral encephalitis. Encephalitis occurred more frequently in older age groups and less frequently in women in Ontario when compared to England, but despite differences in population, vector-borne diseases, climate, and geography, the epidemiology was overall remarkably similar in the two regions.
| S-EPMC4766895 | biostudies-literature
Project description:BackgroundFoliar fungicide applications to corn (Zea mays L.) occur at one or more application timings ranging from early vegetative growth stages to mid-reproductive stages. Previous studies indicated that fungicide applications are profitable under high disease pressure when applied during the tasseling to silking growth stages. Few comprehensive studies in corn have examined the impact of fungicide applications at an early vegetative growth stage (V6) compared to late application timings (VT) for yield response and return on fungicide investment (ROI) across multiple locations.ObjectiveCompare yield response of fungicide application timing across multiple fungicide classes and calculate the probability of positive ROI.MethodsData were collected specifically for this analysis using a uniform protocol conducted in 13 states in the United States and one province in Canada from 2014-2015. Data were subjected to a primary mixed-model analysis of variance. Subsequent univariate meta-analyses, with and without moderator variables, were performed using standard meta-analytic procedures. Follow-up power and prediction analyses were performed to aid interpretation and development of management recommendations.ResultsFungicide application resulted in a range of yield responses from -2,683.0 to 3,230.9 kg/ha relative to the non-treated control, with 68.2% of these responses being positive. Evidence suggests that all three moderator variables tested (application timing, fungicide class, and disease base level), had some effect (α = 0.05) on the absolute difference in yield between fungicide treated and non-treated plots ([Formula: see text]). Application timing influenced [Formula: see text], with V6 + VT and the VT application timings resulting in greater yield responses than the V6 application timing alone. Fungicide formulations that combined demethylation inhibitor and quinone outside inhibitor fungicides significantly increased yield response.ConclusionFoliar fungicide applications can increase corn grain yield. To ensure the likelihood of a positive ROI, farmers should focus on applications at VT and use fungicides that include a mix of demethylation inhibitor and quinone outside inhibitor active ingredients.
| S-EPMC6550426 | biostudies-literature
Project description:Survival implications of nontuberculous mycobacterial pulmonary disease (NTM-PD) and NTM pulmonary isolation without disease (NTM-PI) are unclear. To study deaths associated with NTM-PD and NTM-PI and differences in survival between them, we conducted a population-based cohort study of persons with microbiologically defined NTM-PD or NTM-PI diagnosed during 2001-2013 in Ontario, Canada. We used propensity score matching and Cox proportional hazards models to compare survival. Among 9,681 NTM-PD patients and 10,936 NTM-PI patients, 87% and 91%, respectively, were successfully matched with unexposed controls. Both NTM-PD and NTM-PI were associated with higher rates of death for all species combined and for most individual species. Compared with NTM-PI, NTM-PD was associated with higher death rates for all species combined, Mycobacterium avium complex, and M. xenopi. NTM-PD and NTM-PI were significantly associated with death, NTM-PD more so than NTM-PI.
| S-EPMC5382758 | biostudies-literature
Project description:This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.
| S-EPMC7810832 | biostudies-literature
Project description:Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.
| S-EPMC5104953 | biostudies-literature
Project description:Canada has one of the highest incidence rates of brain cancer in the world. This study investigates the space-time variation of brain cancer risk across Southern Ontario, Canada. A Bayesian spatio-temporal regression model is used to estimate the relative risk of brain cancer in the 12 spatial health units of Southern Ontario over a four-year period (2010-2013). This work also explores the association between brain cancer and two potential risk factors: traumatic head injury (THI) and excess body fat (EBF). Across all areal units from 2010-2013, results show that the relative risk of brain cancer ranged from 0.83 (95% credible interval (CI) 0.74-0.91) to 1.26 (95% CI 1.13-1.41). Over the years, the eastern and western health units had persistently higher risk levels compared to those in the central areas. Results suggest that areas with elevated THI rates and EBF levels were also potentially associated with higher brain cancer relative risk. Findings revealed that the mean temporal trend for cancer risk progression in the region smoothly decreased over time. Overall, 50% of the health units displayed area-specific trends which were higher than the region's average, thus indicating a slower decrease in cancer rates for these areas in comparison to the mean trend.
| S-EPMC6950658 | biostudies-literature
Project description:We quantify long-run adaptation of U.S. corn and soybean yields to changes in temperature and precipitation over 1951-2017. Results show that although the two crops became more heat- and drought-tolerant, their productivity under normal temperature and precipitation conditions decreased. Over 1951-2017, heat- and drought-tolerance increased corn and soybean yields by 33% and 20%, whereas maladaptation to normal conditions reduced yields by 41% and 87%, respectively, with large spatial variations in effects. Changes in climate are projected to reduce average corn and soybean yields by 39-68% and 86-92%, respectively, by 2050 relative to 2013-2017 depending on the warming scenario. After incorporating estimated effects of climate-neutral technological advances, the net change in yield ranges from (-)13 to 62% for corn and (-)57 to (-)26% for soybeans in 2050 relative to 2013-2017. Our analysis uncovers the inherent trade-offs and limitations of existing approaches to crop adaptation.
| S-EPMC8196191 | biostudies-literature
Project description:BackgroundMedical schools with distributed or regional programs encourage people to live, work, and learn in communities that may be economically challenged. Local spending by the program, staff, teachers, and students has a local economic impact. Although the economic impact of DME has been estimated for nations and sub-national regions, the community-specific impact is often unknown. Communities that contribute to the success of DME have an interest in knowing the local economic impact of this participation. To provide this information, we estimated the economic impact of the Northern Ontario School of Medicine (NOSM) on selected communities in the historically medically underserviced and economically disadvantaged Northern Ontario region.MethodsEconomic impact was estimated by a cash-flow local economic model. Detailed data on program and learner spending were obtained for Northern Ontario communities. We included spending on NOSM's distributed education and research programs, medical residents' salary program, the clinical teachers' reimbursement program, and spending by learners. Economic impact was estimated from total spending in the community adjusted by an economic multiplier based on community population size, industry diversity, and propensity to spend locally. Community employment impact was also estimated.ResultsIn 2019, direct program and learner spending in Northern Ontario totalled $64.6 M (million) Canadian Dollars. Approximately 76% ($49.1 M) was spent in the two largest population centres of 122,000 and 165,000 people, with 1-5% ($0.7 M - $3.1 M) spent in communities of 5000-78,000 people. In 2019, total economic impact in Northern Ontario was estimated to be $107 M, with an impact of $38 M and $36 M in the two largest population centres. The remaining $34 M (32%) of the economic impact occurred in smaller communities or within the region. Expressed alternatively as employment impact, the 404 full time equivalent (FTE) positions supported an additional 298 FTE positions in Northern Ontario. NOSM-trained physicians practising in the region added an economic impact of $88 M.ConclusionsBy establishing programs and bringing people to Northern Ontario communities, NOSM added local spending and knowledge-based economic activity to a predominantly resource-based economy. In an economically deprived region, distributed medical education enabled distributed economic impact.
| S-EPMC8191106 | biostudies-literature
Project description:Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement.
| S-EPMC7039445 | biostudies-literature
Project description:Surveys suggest that clinicians diverge from guidelines when treating Mycobacterium avium complex (MAC) pulmonary disease (PD). To determine prescribing patterns, we conducted a cohort study of adults >66 years of age in Ontario, Canada, with MAC or Mycobacterium xenopi PD during 2001-2013. Using linked laboratory and health administrative databases, we studied the first treatment episode (>60 continuous days of >1 of a macrolide, ethambutol, rifamycin, fluoroquinolone, linezolid, inhaled amikacin, or, for M. xenopi, isoniazid). Treatment was prescribed for 24% MAC and 15% of M. xenopi PD patients. Most commonly prescribed was the recommended combination of macrolide, ethambutol, and rifamycin, for 47% of MAC and 36% of M. xenopi PD patients. Among MAC PD patients, 20% received macrolide monotherapy and 33% received regimens associated with emergent macrolide resistance. Although the most commonly prescribed regimen was guidelines-recommended, many regimens prescribed for MAC PD were associated with emergent macrolide resistance.
| S-EPMC6590764 | biostudies-literature