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Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields. We propose a new convolutional neural network model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. We conduct our experiment using data from 1132 counties for corn and 1076 counties for soybean across the United States. Numerical results demonstrate that our proposed method accurately predicts corn and soybean yield from one to four months before the harvest with an MAE being 8.74% and 8.70% of the average yield, respectively, and is competitive to other state-of-the-art approaches.

Across markets accounting for 80 per cent of global diesel vehicle sales, more than a third of diesel nitrogen oxide emissions are in excess of certification limits, causing many deaths. The dangers of diesel emissions Vehicle emissions such as nitrogen oxides contribute to air pollution that can be harmful to human health and the environment. Diesel vehicles produce about 20 per cent of global nitrogen oxide emissions and emit more under real-world operating conditions than during laboratory certification testing. This paper finds that across 11 markets, representing about 80 per cent of global diesel vehicle sales, nitrogen oxide emissions exceed certification limits for one-third of on-road heavy-duty vehicles and for over half of light-duty vehicles. In 2015, the 'excess' diesel-related nitrogen oxide emissions were associated with around 38,000 deaths related to fine particulate matter and ozone worldwide, including roughly 10 per cent of all ozone-related deaths in the European Union member states. The authors find that heavy-duty vehicles are the dominant contributor to excess diesel-related nitrogen oxide emissions and associated health impacts in most regions. Vehicle emissions contribute to fine particulate matter (PM 2.5 ) and tropospheric ozone air pollution, affecting human health 1 , 2 , 3 , 4 , 5 , crop yields 5 , 6 and climate 5 , 7 worldwide. On-road diesel vehicles produce approximately 20 per cent of global anthropogenic emissions of nitrogen oxides (NO x ), which are key PM 2.5 and ozone precursors 8 , 9 . Regulated NO x emission limits in leading markets have been progressively tightened, but current diesel vehicles emit far more NO x under real-world operating conditions than during laboratory certification testing 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 . Here we show that across 11 markets, representing approximately 80 per cent of global diesel vehicle sales, nearly one-third of on-road heavy-duty diesel vehicle emissions and over half of on-road light-duty diesel vehicle emissions are in excess of certification limits. These excess emissions (totalling 4.6 million tons) are associated with about 38,000 PM 2.5 - and ozone-related premature deaths globally in 2015, including about 10 per cent of all ozone-related premature deaths in the 28 European Union member states. Heavy-duty vehicles are the dominant contributor to excess diesel NO x emissions and associated health impacts in almost all regions. Adopting and enforcing next-generation standards (more stringent than Euro 6/VI) could nearly eliminate real-world diesel-related NO x emissions in these markets, avoiding approximately 174,000 global PM 2.5 - and ozone-related premature deaths in 2040. Most of these benefits can be achieved by implementing Euro VI standards where they have not yet been adopted for heavy-duty vehicles.

Opioids are the primary drugs used in Western medicine for pain management and palliative care. Farming of opium poppies remains the sole source of these essential medicines, despite diverse market demands and uncertainty in crop yields due to weather, climate change, and pests. We engineered yeast to produce the selected opioid compounds thebaine and hydrocodone starting from sugar. All work was conducted in a laboratory that is permitted and secured for work with controlled substances. We combined enzyme discovery, enzyme engineering, and pathway and strain optimization to realize full opiate biosynthesis in yeast. The resulting opioid biosynthesis strains required the expression of 21 (thebaine) and 23 (hydrocodone) enzyme activities from plants, mammals, bacteria, and yeast itself. This is a proof of principle, and major hurdles remain before optimization and scale-up could be achieved. Open discussions of options for governing this technology are also needed in order to responsibly realize alternative supplies for these medically relevant compounds.

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.

The current trajectory for crop yields is insufficient to nourish the world’s population by 2050 1 . Greater and more consistent crop production must be achieved against a backdrop of climatic stress that limits yields, owing to shifts in pests and pathogens, precipitation, heat-waves and other weather extremes. Here we consider the potential of plant sciences to address post-Green Revolution challenges in agriculture and explore emerging strategies for enhancing sustainable crop production and resilience in a changing climate. Accelerated crop improvement must leverage naturally evolved traits and transformative engineering driven by mechanistic understanding, to yield the resilient production systems that are needed to ensure future harvests. Genetic strategies for improving the yield and sustainability of agricultural crops, and the resilience of crops in the face of biotic and abiotic stresses contingent on projected climate change, are evaluated.

Crop yields are critically dependent on weather. A growing empirical literature models this relationship in order to project climate change impacts on the sector. We describe an approach to yield modeling that uses a semiparametric variant of a deep neural network, which can simultaneously account for complex nonlinear relationships in high-dimensional datasets, as well as known parametric structure and unobserved cross-sectional heterogeneity. Using data on corn yield from the US Midwest, we show that this approach outperforms both classical statistical methods and fully-nonparametric neural networks in predicting yields of years withheld during model training. Using scenarios from a suite of climate models, we show large negative impacts of climate change on corn yield, but less severe than impacts projected using classical statistical methods. In particular, our approach is less pessimistic in the warmest regions and the warmest scenarios.

Potentially beneficial microorganisms have been inoculated into agricultural soils for years. However, concurrent with sequencing advances and successful manipulation of host-associated microbiomes, industry and academia have recently boosted investments into microbial inoculants, convinced they can increase crop yield and reduce fertilizer and pesticide requirements. The efficacy of soil microbial inoculants remains unreliable, and unlike crop breeding, in which target traits (e.g., yield) have long been considered alongside environmental compatibility, microbial inoculant ecology is not sufficiently integrated into microbial selection and production. We propose a holistic temporal model of the shifting constraints on inoculants at five stages of product development and application, and highlight potential conflicts between stages. We question the feasibility of developing ideal soil microbial inoculants with current approaches. Certain soil microorganisms can perform agriculturally valuable functions such as ethylene reduction, plant pathogen suppression, and soil nutrient solubilization. Interest and investment in developing soil microbial inoculants to enhance these functions has recently surged, but in-field product success remains unpredictable and unreliable. Microbial inoculants tend to be chosen based on their activity in controlled laboratory screenings and for ease of mass cultivation, with minimal regard for ecologically relevant traits that will both allow them to survive in the field during a target functional period and prevent excessive persistence. We highlight the conflicting roles of microbial inoculant traits at each product stage, and how this may complicate selection for microorganisms that function as desired in the field.

Plant RuBisCo, a complex of eight large and eight small subunits, catalyzes the fixation of CO₂ in photosynthesis. The low catalytic efficiency of RuBisCo provides strong motivation to reengineer the enzyme with the goal of increasing crop yields. However, genetic manipulation has been hampered by the failure to express plant RuBisCo in a bacterial host. We achieved the functional expression of Arabidopsis thaliana RuBisCo in Escherichia coli by coexpressing multiple chloroplast chaperones. These include the chaperonins Cpn60/Cpn20, RuBisCo accumulation factors 1 and 2, RbcX, and bundle-sheath defective-2 (BSD2). Our structural and functional analysis revealed the role of BSD2 in stabilizing an end-state assembly intermediate of eight RuBisCo large subunits until the small subunits become available. The ability to produce plant RuBisCo recombinantly will facilitate efforts to improve the enzyme through mutagenesis.

Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factors-including mean climate as well as climate extremes-explain 20%-49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%-43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes.

Applying biochar to soil is thought to have multiple benefits, from helping mitigate climate change [1, 2], to managing waste [3] to conserving soil [4]. Biochar is also widely assumed to boost crop yield [5, 6], but there is controversy regarding the extent and cause of any yield benefit [7]. Here we use a global-scale meta-analysis to show that biochar has, on average, no effect on crop yield in temperate latitudes, yet elicits a 25% average increase in yield in the tropics. In the tropics, biochar increased yield through liming and fertilization, consistent with the low soil pH, low fertility, and low fertilizer inputs typical of arable tropical soils. We also found that, in tropical soils, high-nutrient biochar inputs stimulated yield substantially more than low-nutrient biochar, further supporting the role of nutrient fertilization in the observed yield stimulation. In contrast, arable soils in temperate regions are moderate in pH, higher in fertility, and generally receive higher fertilizer inputs, leaving little room for additional benefits from biochar. Our findings demonstrate that the yield-stimulating effects of biochar are not universal, but may especially benefit agriculture in low-nutrient, acidic soils in the tropics. Biochar management in temperate zones should focus on potential non-yield benefits such as lime and fertilizer cost savings, greenhouse gas emissions control, and other ecosystem services.