The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

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Covasim: An agent-based model of COVID-19 dynamics and interventions.

This article discusses the unequal distribution of food within and among countries of the world poverty as the main cause of hunger the patterns of population growth and future prospects. The evidence reveals the potential for reaching limits of agricultural expansion. Widespread environmental destruction suggests that agricultural lands are declining. Current production patterns may not be sustainable. "Population growth is the single largest determinant of future needs." Farmers of the worlds poorest regions must produce food to meet the needs of a doubled population by 2050. The 1996 World Food Summit urged that agricultural policies emphasize environmentally sustainable production methods and a wider variety of crops that would include sorghum and millet. In the early 1990s 40% of Africas population was food-energy deficient. Africa has some of the highest population growth rates. Fertility stands at about 6 children/woman. Even with reduced fertility the African population is expected to double to 22% of world population by 2050. The East Asian population which is dominated by China is expected to reach 2.2 billion by 2050. South Asia includes some of the poorest and most densely populated countries; its population is expected to reach 2.2-3.3 billion by 2050. 43% of the malnourished population during 1990-92 lived in sub-Saharan Africa 22% lived in South Asia 16% lived in East and Southeast Asia 15% lived in Latin America and the Caribbean and 12% lived in the Near East and North Africa. The proportions of underweight children included 58% in South Asia 30% in sub-Saharan Africa 25% in the Near East and North Africa 24% in East and Southeast Asia and 12% in Latin America and the Caribbean. If everyone adopted a vegetarian diet and no food were wasted there would be enough food to feed 10 billion people. Malnourishment has the harshest effects on children rural populations the growing urban poor and victims of natural disasters.

Feeding the future.

Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Simulation crop models can assist in scenario planning, but their use is limited because of data requirements and long runtimes. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of four machine learning (ML) algorithms (LASSO Regression, Ridge Regression, random forests, Extreme Gradient Boosting, and their ensembles) as meta-models for a cropping systems simulator (APSIM) to inform future decision support tool development. We asked: (1) How well do ML meta-models predict maize yield and N losses using pre-season information? (2) How many data are needed to train ML algorithms to achieve acceptable predictions? (3) Which input data variables are most important for accurate prediction? And (4) do ensembles of ML meta-models improve prediction? The simulated dataset included more than three million data including genotype, environment and management scenarios. XGBoost was the most accurate ML model in predicting yields with a relative mean square error (RRMSE) of 13.5%, and Random forests most accurately predicted N loss at planting time, with a RRMSE of 54%. ML meta-models reasonably reproduced simulated maize yields using the information available at planting, but not N loss. They also differed in their sensitivities to the size of the training dataset. Across all ML models, yield prediction error decreased by 10%–40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. ML models also differed in their sensitivities to input variables (weather, soil properties, management, initial conditions), thus depending on the data availability researchers may use a different ML model. Modest prediction improvements resulted from ML ensembles. These results can help accelerate progress in coupling simulation models and ML toward developing dynamic decision support tools for pre-season management.

https://iopscience.iop.org/article/10.1088/1748-9326/ab5268/pdf

Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms

Price discount facility in an EOQ model for deteriorating items with stock‐dependent demand and partial backlogging

The emergence of new technologies to synthesize and analyze big data with high-performance computing has increased our capacity to more accurately predict crop yields. Recent research has shown that machine learning (ML) can provide reasonable predictions faster and with higher flexibility compared to simulation crop modeling. However, a single machine learning model can be outperformed by a "committee" of models (machine learning ensembles) that can reduce prediction bias, variance, or both and is able to better capture the underlying distribution of the data. Yet, there are many aspects to be investigated with regard to prediction accuracy, time of the prediction, and scale. The earlier the prediction during the growing season the better, but this has not been thoroughly investigated as previous studies considered all data available to predict yields. This paper provides a machine leaning based framework to forecast corn yields in three US Corn Belt states (Illinois, Indiana, and Iowa) considering complete and partial in-season weather knowledge. Several ensemble models are designed using blocked sequential procedure to generate out-of-bag predictions. The forecasts are made in county-level scale and aggregated for agricultural district and state level scales. Results show that the proposed optimized weighted ensemble and the average ensemble are the most precise models with RRMSE of 9.5%. Stacked LASSO makes the least biased predictions (MBE of 53 kg/ha), while other ensemble models also outperformed the base learners in terms of bias. On the contrary, although random k-fold cross-validation is replaced by blocked sequential procedure, it is shown that stacked ensembles perform not as good as weighted ensemble models for time series data sets as they require the data to be non-IID to perform favorably. Comparing our proposed model forecasts with the literature demonstrates the acceptable performance of forecasts made by our proposed ensemble model. Results from the scenario of having partial in-season weather knowledge reveals that decent yield forecasts with RRMSE of 9.2% can be made as early as June 1st. Moreover, it was shown that the proposed model performed better than individual models and benchmark ensembles at agricultural district and state-level scales as well as county-level scale. To find the marginal effect of each input feature on the forecasts made by the proposed ensemble model, a methodology is suggested that is the basis for finding feature importance for the ensemble model. The findings suggest that weather features corresponding to weather in weeks 18-24 (May 1st to June 1st) are the most important input features.

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Forecasting Corn Yield With Machine Learning Ensembles

Pricing and quality level decisions of substitutable products in online and traditional selling channels: game-theoretical approaches

Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method was used to comprehensively evaluate physicochemical properties of fried cheese nuggets so that chitosan (0, 0.5 and 1.5%) was added to batter formulation under different processing conditions namely frying temperature (150, 170 and 190 °C) and time (0–4 min). A non-destructive, image-based method was used to measured mechanical properties of fried, breaded cheese nuggets. The results of this study indicated that all the batters have shear-thinning behavior and the coating pickup was found to be directly proportional to batter viscosity. The highest reduction in oil uptake (22.7%) was observed in samples contain 1.5% chitosan. The deep fried cheese nuggets containing chitosan tended to be higher in hardness and maximum sound peak and lower in porosity in comparison to the control sample. The optimum conditions resulting in desirable physico-chemical properties and minimum oil uptake were cheese nuggets with chitosan content of 1.5%, fried at a temperature of 170 °C for 4 min.

A new technique to evaluate the effect of chitosan on properties of deep-fried Kurdish cheese nuggets by TOPSIS

Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agronomic insight, we present a new predictive model (referred to as the interaction regression model) for crop yield prediction, which has three salient properties. First, it achieved a relative root mean square error of 8% or less in three Midwest states (Illinois, Indiana, and Iowa) in the US for both corn and soybean yield prediction, outperforming state-of-the-art machine learning algorithms. Second, it identified about a dozen environment by management interactions for corn and soybean yield, some of which are consistent with conventional agronomic knowledge whereas some others interactions require additional analysis or experiment to prove or disprove. Third, it quantitatively dissected crop yield into contributions from weather, soil, management, and their interactions, allowing agronomists to pinpoint the factors that favorably or unfavorably affect the yield of a given location under a given weather and management scenario. The most significant contribution of the new prediction model is its capability to produce accurate prediction and explainable insights simultaneously. This was achieved by training the algorithm to select features and interactions that are spatially and temporally robust to balance prediction accuracy for the training data and generalizability to the test data.

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An interaction regression model for crop yield prediction.

Performance prediction of potential crosses plays a significant role in plant breeding, which aims to produce new crop varieties that have higher yields, require fewer resources, and are more adaptable to the changing environments. In the 2020 Syngenta crop challenge, Syngenta challenged participants to predict the yield performance of a list of potential breeding crosses of inbreds and testers based on their historical yield data in different environments. They released a dataset that contained the observed yields for 294,128 corn hybrids through the crossing of 593 unique inbreds and 496 unique testers across multiple environments between 2016 and 2018. To address this challenge, we designed a new predictive approach that integrates random forest and an optimization model for G [Formula: see text] E interaction detection. Our computational experiment found that our approach achieved a relative root-mean-square-error (RMSE) of 0.0869 for the validation data, outperforming other state-of-the-art models such as factorization machine and extreme gradient boosting tree. Our model was also able to detect genotype by environment interactions that are potentially biologically insightful. This model won the first place in the 2020 Syngenta crop challenge in analytics.

/pdf/performance-prediction-of-crosses-in-plant-breeding-through-2u3niz6j0q.pdf

Performance prediction of crosses in plant breeding through genotype by environment interactions

MOTIVATION Epistasis, which is the phenomenon of genetic interactions, plays a central role in many scientific discoveries. However, due to the combinatorial nature of the problem, it is extremely challenging to decipher the exact combinations of genes that trigger the epistatic effects. Many existing methods only focus on two-way interactions. Some of the most effective methods used machine learning techniques, but many were designed for special case-and-control studies or suffer from overfitting. We propose three new algorithms for multi-effect and multi-way epistases detection, with one guaranteeing global optimality and the other two being local optimization oriented heuristics. RESULTS The computational performance of the proposed heuristic algorithm was compared with several state-of-the-art methods using a yeast dataset. Results suggested that searching for the global optimal solution could be extremely time consuming, but the proposed heuristic algorithm was much more effective and efficient than others at finding a close-to-optimal solution. Moreover, it was able to provide biological insight on the exact configurations of epistases, besides achieving a higher prediction accuracy than the state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION Data source was publicly available and details are provided in the text.

Javad Ansarifar

Papers

Pricing and quality level decisions of substitutable products in online and traditional selling channels: game-theoretical approaches

A new technique to evaluate the effect of chitosan on properties of deep-fried Kurdish cheese nuggets by TOPSIS

An interaction regression model for crop yield prediction.

Performance prediction of crosses in plant breeding through genotype by environment interactions

New algorithms for detecting multi-effect and multi-way epistatic interactions.