Species-independent analytical tools for next-generation agriculture

Innovative approaches are urgently required to alleviate the growing pressure on agriculture to meet the rising demand for food. A key challenge for plant biology is to bridge the notable knowledge gap between our detailed understanding of model plants grown under laboratory conditions and the agriculturally important crops cultivated in fields or production facilities. This Perspective highlights the recent development of new analytical tools that are rapid and non-destructive and provide tissue-, cell- or organelle-specific information on living plants in real time, with the potential to extend across multiple species in field applications. We evaluate the utility of engineered plant nanosensors and portable Raman spectroscopy to detect biotic and abiotic stresses, monitor plant hormonal signalling as well as characterize the soil, phytobiome and crop health in a non- or minimally invasive manner. We propose leveraging these tools to bridge the aforementioned fundamental gap with new synthesis and integration of expertise from plant biology, engineering and data science. Lastly, we assess the economic potential and discuss implementation strategies that will ensure the acceptance and successful integration of these modern tools in future farming practices in traditional as well as urban agriculture.

[1]  Narangerel Altangerel,et al.  In vivo diagnostics of early abiotic plant stress response via Raman spectroscopy , 2017, Proceedings of the National Academy of Sciences.

[2]  C. Gutjahr,et al.  Systems Biology of Plant-Microbiome Interactions. , 2019, Molecular plant.

[3]  Hezi Tenenboim,et al.  Omic Relief for the Biotically Stressed: Metabolomics of Plant Biotic Interactions. , 2016, Trends in plant science.

[4]  M. Dauzat,et al.  PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments , 2017, Plant Methods.

[5]  Á. Fernández-Recamales,et al.  Nutritional and nutraceutical quality of strawberries in relation to harvest time and crop conditions. , 2014, Journal of agricultural and food chemistry.

[6]  Magdalena Sawicka,et al.  Urban agriculture of the future: an overview of sustainability aspects of food production in and on buildings , 2014 .

[7]  Y. Elad,et al.  Climate Change Impacts on Plant Pathogens and Plant Diseases , 2014 .

[8]  Wenjun Di,et al.  Optical Nanosensors for in vivo Physiological Chloride Detection for Monitoring Cystic Fibrosis Treatment. , 2020, Analytical methods : advancing methods and applications.

[9]  S. Swarup,et al.  Crosskingdom growth benefits of fungus-derived phytohormones in Choy Sum , 2020, bioRxiv.

[10]  S. Robinson,et al.  Food Security: The Challenge of Feeding 9 Billion People , 2010, Science.

[11]  Q. Wei,et al.  Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles , 2019, Nature Plants.

[12]  P. Borrill Blurring the boundaries between cereal crops and model plants. , 2020, The New phytologist.

[13]  M. Kenis,et al.  Safeguarding global plant health: the rise of sentinels , 2018, Journal of Pest Science.

[14]  Xiaoyi Shan,et al.  Comparison of phytohormone signaling mechanisms. , 2012, Current opinion in plant biology.

[15]  Sonia J. Miller,et al.  The metabolic transition during disease following infection of Arabidopsis thaliana by Pseudomonas syringae pv. tomato. , 2010, The Plant journal : for cell and molecular biology.

[16]  L. Xiong,et al.  Crop Phenomics and High-throughput Phenotyping: Past Decades, Current Challenges and Future Perspectives. , 2020, Molecular plant.

[17]  Francisco Rovira-Más,et al.  From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management , 2020, Agronomy.

[18]  D. Inzé,et al.  Cell to whole-plant phenotyping: the best is yet to come. , 2013, Trends in plant science.

[19]  Stavros Souravlas,et al.  Data-Driven Decision Making in Precision Agriculture: The Rise of Big Data in Agricultural Systems , 2019, Journal of Agricultural & Food Information.

[20]  A. Fernie,et al.  The use of metabolomics to dissect plant responses to abiotic stresses , 2012, Cellular and Molecular Life Sciences.

[21]  J. Morrell-Falvey,et al.  Raman chemical imaging of the rhizosphere bacterium Pantoea sp. YR343 and its co-culture with Arabidopsis thaliana. , 2016, The Analyst.

[22]  R. Garrido-Oter,et al.  Balancing trade-offs between biotic and abiotic stress responses through leaf age-dependent variation in stress hormone cross-talk , 2019, Proceedings of the National Academy of Sciences.

[23]  Aakash Chawade,et al.  High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture , 2019, Agronomy.

[24]  N. Ramankutty,et al.  Influence of extreme weather disasters on global crop production , 2016, Nature.

[25]  I. Lynch,et al.  Corona of Thorns: The Surface Chemistry-Mediated Protein Corona Perturbs the Recognition and Immune Response of Macrophage. , 2019, ACS applied materials & interfaces.

[26]  Volodymyr B. Koman,et al.  Rational Design Principles for the Transport and Subcellular Distribution of Nanomaterials into Plant Protoplasts. , 2018, Small.

[27]  Jungwon Yoon,et al.  The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community , 2003, Nucleic Acids Res..

[28]  K. Numata,et al.  Targeted Gene Delivery into Various Plastids Mediated by Clustered Cell‐Penetrating and Chloroplast‐Targeting Peptides , 2019, Advanced science.

[29]  Pouria Sadeghi-Tehran,et al.  Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering , 2017, Front. Plant Sci..

[30]  M. Bevan,et al.  The Arabidopsis genome: a foundation for plant research. , 2005, Genome research.

[31]  Achim Walter,et al.  Comparison of visible imaging, thermography and spectrometry methods to evaluate the effect of Heterodera schachtii inoculation on sugar beets , 2017, Plant Methods.

[32]  Elisabeth Georgii,et al.  Monoterpenes Support Systemic Acquired Resistance within and between Plants , 2017, Plant Cell.

[33]  Praveen Kumar,et al.  Rhizosphere microbiome: revisiting the synergy of plant-microbe interactions , 2019, Annals of Microbiology.

[34]  Kun Wu,et al.  Modulating plant growth-metabolism coordination for sustainable agriculture , 2018, Nature.

[35]  R. Kookana,et al.  A critical evaluation of nanopesticides and nanofertilizers against their conventional analogues , 2018, Nature Nanotechnology.

[36]  Heping Zhu,et al.  Plant Pest Detection Using an Artificial Nose System: A Review , 2018, Sensors.

[37]  Nobuhiro Suzuki,et al.  ROS, Calcium, and Electric Signals: Key Mediators of Rapid Systemic Signaling in Plants1[OPEN] , 2016, Plant Physiology.

[38]  Ming Yi,et al.  Mechanisms of ROS Regulation of Plant Development and Stress Responses , 2019, Front. Plant Sci..

[39]  Kurt K. Benke,et al.  Future food-production systems: vertical farming and controlled-environment agriculture , 2017 .

[40]  Y. Cohen,et al.  Estimation of leaf water potential by thermal imagery and spatial analysis. , 2005, Journal of experimental botany.

[41]  Charles Farber,et al.  Advanced spectroscopic techniques for plant disease diagnostics. A review , 2019, TrAC Trends in Analytical Chemistry.

[42]  Chunjiang Zhao,et al.  Crop Phenomics: Current Status and Perspectives , 2019, Front. Plant Sci..

[43]  M. Kenis,et al.  Sentinel nurseries to assess the phytosanitary risks from insect pests on importations of live plants , 2018, Scientific Reports.

[44]  N. Dudareva,et al.  Plant Volatiles: Recent Advances and Future Perspectives , 2006 .

[45]  R. Mittler,et al.  Whole-plant live imaging of reactive oxygen species. , 2019, Molecular plant.

[46]  Jian‐Kang Zhu Abiotic Stress Signaling and Responses in Plants , 2016, Cell.

[47]  Camila Caldana,et al.  Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress. , 2016, Mass spectrometry reviews.

[48]  Achim Walter,et al.  The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. , 2016, Functional plant biology : FPB.

[49]  M. Mihara,et al.  Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions , 2012, Cell.

[50]  Carolin Seyfferth,et al.  Salicylic acid signal transduction: the initiation of biosynthesis, perception and transcriptional reprogramming , 2014, Front. Plant Sci..

[51]  Alexander M. Jones,et al.  Genetically Encoded Biosensors in Plants: Pathways to Discovery. , 2018, Annual review of plant biology.

[52]  Chengzhou Zhu,et al.  Electrochemical Sensors and Biosensors Based on Nanomaterials and Nanostructures , 2014, Analytical chemistry.

[53]  S. Hacquard,et al.  Microbial interactions within the plant holobiont , 2018, Microbiome.

[54]  A. Dai Increasing drought under global warming in observations and models , 2013 .

[55]  S. Kruss,et al.  Impact of Redox-Active Molecules on the Fluorescence of Polymer-Wrapped Carbon Nanotubes , 2016 .

[56]  S. Kay,et al.  A Genomic Analysis of the Shade Avoidance Response in Arabidopsis1[w] , 2003, Plant Physiology.

[57]  Anne-Katrin Mahlein Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. , 2016, Plant disease.

[58]  A. Fernie,et al.  Gas chromatography mass spectrometry–based metabolite profiling in plants , 2006, Nature Protocols.

[59]  S. Jackson Rice: The First Crop Genome , 2016, Rice.

[60]  Markita P Landry,et al.  Nanoparticle-Mediated Delivery towards Advancing Plant Genetic Engineering. , 2018, Trends in biotechnology.

[61]  P. Ciais,et al.  The impacts of climate change on water resources and agriculture in China , 2010, Nature.

[62]  Benno I. Simmons,et al.  The environmental costs and benefits of high-yield farming , 2018, Nature Sustainability.

[63]  K. Shinozaki,et al.  Crosstalk between abiotic and biotic stress responses: a current view from the points of convergence in the stress signaling networks. , 2006, Current opinion in plant biology.

[64]  V. Zhdanov Formation of a protein corona around nanoparticles , 2019, Current Opinion in Colloid & Interface Science.

[65]  Yi Zhang,et al.  Efficient and transgene-free genome editing in wheat through transient expression of CRISPR/Cas9 DNA or RNA , 2016, Nature Communications.

[66]  J. Liesche,et al.  The molecular mechanism of shade avoidance in crops- How data from Arabidopsis can help to identify targets for increasing yield and biomass production , 2017 .

[67]  Precision Agriculture and Remote Sensing , 2015 .

[68]  R. Mittler,et al.  Orchestrating rapid long-distance signaling in plants with Ca2+ , ROS and electrical signals. , 2017, The Plant journal : for cell and molecular biology.

[69]  Michael Wagner,et al.  Capturing the genetic makeup of the active microbiome in situ , 2017, The ISME Journal.

[70]  Yingliang Liu,et al.  Phytotoxicity, Uptake, and Translocation of Fluorescent Carbon Dots in Mung Bean Plants. , 2016, ACS applied materials & interfaces.

[71]  Genesis Berlanga,et al.  Remote Raman measurements of minerals, organics, and inorganics at 430  m range. , 2016, Applied optics.

[72]  Achim Walter,et al.  Opinion: Smart farming is key to developing sustainable agriculture , 2017, Proceedings of the National Academy of Sciences.

[73]  G. Caldarelli,et al.  The network of plants volatile organic compounds , 2017, Scientific Reports.

[74]  Volodymyr B. Koman,et al.  Chloroplast-selective gene delivery and expression in planta using chitosan-complexed single-walled carbon nanotube carriers , 2019, Nature Nanotechnology.

[75]  J. P. Giraldo,et al.  Standoff Optical Glucose Sensing in Photosynthetic Organisms by a Quantum Dot Fluorescent Probe. , 2018, ACS applied materials & interfaces.

[76]  Thomas Lagkas,et al.  A compilation of UAV applications for precision agriculture , 2020, Comput. Networks.

[77]  M. Lefsrud,et al.  Tomato proteomics: Tomato as a model for crop proteomics , 2018, Scientia Horticulturae.

[78]  A. Pereira Plant Abiotic Stress Challenges from the Changing Environment , 2016, Front. Plant Sci..

[79]  Thomas Udelhoven,et al.  Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review , 2019, Remote. Sens..

[80]  Y. Choi,et al.  NMR-based plant metabolomics: where do we stand, where do we go? , 2011, Trends in biotechnology.

[81]  Y. Onoda,et al.  Core microbiomes for sustainable agroecosystems , 2018, Nature Plants.

[82]  Masayuki Hirafuji,et al.  Field monitoring support system for the occurrence of Leptocorisa chinensis Dallas (Hemiptera: Alydidae) using synthetic attractants, Field Servers, and image analysis , 2012 .

[83]  Hanhong Bae,et al.  Genomics and evolutionary aspect of calcium signaling event in calmodulin and calmodulin-like proteins in plants , 2017, BMC Plant Biology.

[84]  Xiuxiu Sun,et al.  Changes in flavor-relevant compounds during vine ripening of tomato fruit and their relationship with ethylene production , 2018, Horticulture, Environment, and Biotechnology.

[85]  Wolfgang J Parak,et al.  A Decade of the Protein Corona. , 2017, ACS nano.

[86]  Akira Fujishima,et al.  Highly Sensitive Measurement of Bio-Electric Potentials by Boron-Doped Diamond (BDD) Electrodes for Plant Monitoring , 2015, Sensors.

[87]  Yang Liu,et al.  Insights into the Origin and Evolution of the Plant Hormone Signaling Machinery1 , 2015, Plant Physiology.

[88]  Sonja J. Prohaska,et al.  Ecological plant epigenetics: Evidence from model and non-model species, and the way forward , 2017, bioRxiv.

[89]  Jeffrey W. White,et al.  Rising Temperatures Reduce Global Wheat Production , 2015 .

[90]  Leanne M. Gilbertson,et al.  Opportunities and challenges for nanotechnology in the agri-tech revolution , 2019, Nature Nanotechnology.

[91]  Zhiqiang Lai,et al.  Full-color emissive carbon-dots targeting cell walls of onion for in situ imaging of heavy metal pollution. , 2019, The Analyst.

[92]  Yufeng Ge,et al.  A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding , 2016, Comput. Electron. Agric..

[93]  Thea King,et al.  Food safety for food security: Relationship between global megatrends and developments in food safety , 2017 .

[94]  Freddy T. Nguyen,et al.  A Fiber Optic Interface Coupled to Nanosensors: Applications to Protein Aggregation and Organic Molecule Quantification. , 2020, ACS nano.

[95]  T. Swager,et al.  Trace Ethylene Sensing via Wacker Oxidation , 2020, ACS central science.

[96]  Leanne Bischof,et al.  A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field , 2016, Journal of experimental botany.

[97]  A. Fernie,et al.  Metabolite profiling: from diagnostics to systems biology , 2004, Nature Reviews Molecular Cell Biology.

[98]  Nathaniel R. Gomer,et al.  Remote Raman Spectroscopy for Planetary Exploration: A Review , 2012, Applied spectroscopy.

[99]  C. Pipper,et al.  Reducing shade avoidance responses in a cereal crop , 2017, AoB PLANTS.

[100]  K. Numata,et al.  Selective Gene Delivery for Integrating Exogenous DNA into Plastid and Mitochondrial Genomes Using Peptide-DNA Complexes. , 2018, Biomacromolecules.

[101]  M. Strano,et al.  Nanocarriers for Transgene Expression in Pollen as a Plant Biotechnology Tool , 2020 .

[102]  U. Hoecker,et al.  Arabidopsis COP1 and SPA Genes Are Essential for Plant Elongation But Not for Acceleration of Flowering Time in Response to a Low Red Light to Far-Red Light Ratio1[W] , 2012, Plant Physiology.

[103]  J. Araus,et al.  Field high-throughput phenotyping: the new crop breeding frontier. , 2014, Trends in plant science.

[104]  L. Prokopy,et al.  Why farmers adopt best management practice in the United States: a meta-analysis of the adoption literature. , 2012, Journal of environmental management.

[105]  A. Kushalappa,et al.  Metabolo-proteomics to discover plant biotic stress resistance genes. , 2013, Trends in plant science.

[106]  S. Kapoor,et al.  Rice Improvement Through Genome-Based Functional Analysis and Molecular Breeding in India , 2016, Rice.

[107]  Francis M. Epplin,et al.  The economic potential of precision nitrogen application with wheat based on plant sensing , 2009 .

[108]  R. K. Sharma,et al.  Next Generation Sequencing Technologies: The Doorway to the Unexplored Genomics of Non-Model Plants , 2015, Front. Plant Sci..

[109]  Jenny Renaut,et al.  Proteome analysis of non-model plants: a challenging but powerful approach. , 2008, Mass spectrometry reviews.

[110]  Qin Zhang,et al.  A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.

[111]  Jennifer D. Lewis,et al.  The enemy within: phloem-limited pathogens. , 2018, Molecular plant pathology.

[112]  Daniel B. Müller,et al.  The Plant Microbiota: Systems-Level Insights and Perspectives. , 2016, Annual review of genetics.

[113]  C. Casteel,et al.  Vector-Borne Bacterial Plant Pathogens: Interactions with Hemipteran Insects and Plants , 2016, Front. Plant Sci..

[114]  H. Scharr,et al.  phenoSeeder - A Robot System for Automated Handling and Phenotyping of Individual Seeds1[OPEN] , 2016, Plant Physiology.

[115]  Z. Qi,et al.  Teaching an Old Hormone New Tricks: Cytosolic Ca2+ Elevation Involvement in Plant Brassinosteroid Signal Transduction Cascades1[W][OPEN] , 2013, Plant Physiology.

[116]  Andy Lin,et al.  PlantCV v2: Image analysis software for high-throughput plant phenotyping , 2017, PeerJ.

[117]  J. Giovannoni,et al.  Genetics and control of tomato fruit ripening and quality attributes. , 2011, Annual review of genetics.

[118]  Jianbing Yan,et al.  Genome assembly of a tropical maize inbred line provides insights into structural variation and crop improvement , 2019, Nature Genetics.

[119]  Neil McRoberts,et al.  The global burden of pathogens and pests on major food crops , 2019, Nature Ecology & Evolution.

[120]  Ardemis A. Boghossian,et al.  Plant nanobionics approach to augment photosynthesis and biochemical sensing. , 2014, Nature materials.

[121]  S. Long,et al.  Meeting the Global Food Demand of the Future by Engineering Crop Photosynthesis and Yield Potential , 2015, Cell.

[122]  K. Nagel,et al.  Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities. , 2019, Trends in plant science.

[123]  Marilena Hadjidemetriou,et al.  Nanomedicine: Evolution of the nanoparticle corona. , 2017, Nature nanotechnology.

[124]  Jörg-Peter Schnitzler,et al.  Practical approaches to plant volatile analysis. , 2006, The Plant journal : for cell and molecular biology.

[125]  Leanne M. Gilbertson,et al.  Technology readiness and overcoming barriers to sustainably implement nanotechnology-enabled plant agriculture , 2020, Nature Food.

[126]  J. Eisen,et al.  Research priorities for harnessing plant microbiomes in sustainable agriculture , 2017, PLoS biology.

[127]  L. G. Santesteban,et al.  High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard , 2017 .

[128]  Alisdair R Fernie,et al.  Application of GC-MS for the detection of lipophilic compounds in diverse plant tissues , 2009, Plant Methods.

[129]  Xiaohu Gao,et al.  Designing multifunctional quantum dots for bioimaging, detection, and drug delivery. , 2010, Chemical Society reviews.

[130]  Cristina E. Davis,et al.  Advanced methods of plant disease detection. A review , 2014, Agronomy for Sustainable Development.

[131]  T. Mockler,et al.  High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. , 2017, Current opinion in plant biology.

[132]  Michael S. Strano,et al.  The Emergence of Plant Nanobionics and Living Plants as Technology , 2019, Advanced Materials Technologies.

[133]  M. Kummu,et al.  Feeding ten billion people is possible within four terrestrial planetary boundaries , 2020, Nature Sustainability.

[134]  D. Does,et al.  Genetic modification to improve disease resistance in crops , 2019, The New phytologist.

[135]  Alice C. McHardy,et al.  Functional overlap of the Arabidopsis leaf and root microbiota , 2015, Nature.

[136]  R. Prasanna,et al.  Prospecting the characteristics and significance of the phyllosphere microbiome , 2018, Annals of Microbiology.

[137]  Gajendra Pratap Singh,et al.  Early Diagnosis and Management of Nitrogen Deficiency in Plants Utilizing Raman Spectroscopy , 2020, Frontiers in Plant Science.

[138]  Amir Kaplan,et al.  Nanosensor Technology Applied to Living Plant Systems. , 2017, Annual review of analytical chemistry.

[139]  J. Reyes-De-Corcuera,et al.  GC-MS metabolomic differentiation of selected citrus varieties with different sensitivity to citrus huanglongbing. , 2012, Plant physiology and biochemistry : PPB.

[140]  Ashutosh Kumar Singh,et al.  Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives. , 2018, Trends in plant science.

[141]  L. Xiong,et al.  Novel Digital Features Discriminate Between Drought Resistant and Drought Sensitive Rice Under Controlled and Field Conditions , 2018, Front. Plant Sci..

[142]  Volodymyr B. Koman,et al.  Nitroaromatic detection and infrared communication from wild-type plants using plant nanobionics. , 2017, Nature materials.

[143]  Hao Yang,et al.  Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives , 2017, Front. Plant Sci..

[144]  Volodymyr B. Koman,et al.  Real-time detection of wound-induced H2O2 signalling waves in plants with optical nanosensors , 2020, Nature Plants.

[145]  Juan Pablo Giraldo,et al.  Nanobiotechnology approaches for engineering smart plant sensors , 2019, Nature Nanotechnology.

[146]  U. Schurr,et al.  Plant Phenotyping: Past, Present, and Future , 2019, Plant phenomics.

[147]  S. Robinson,et al.  Climate Change, Agriculture and Food Security , 2019, Sustainable Food and Agriculture.

[148]  Patricia Garrido,et al.  Handheld Raman spectroscopy for the early detection of plant diseases: Abutilon mosaic virus infecting Abutilon sp. , 2016 .

[149]  C. Ballaré,et al.  The shade-avoidance syndrome: multiple signals and ecological consequences. , 2017, Plant, cell & environment.

[150]  Miguel A Blázquez,et al.  Evolution of Plant Hormone Response Pathways. , 2020, Annual review of plant biology.

[151]  Yiannis Ampatzidis,et al.  UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence , 2019, Remote. Sens..

[152]  A. Kushalappa,et al.  Integrated Metabolo-Proteomic Approach to Decipher the Mechanisms by Which Wheat QTL (Fhb1) Contributes to Resistance against Fusarium graminearum , 2012, PloS one.

[153]  Shauhrat S Chopra,et al.  A framework for sustainable nanomaterial selection and design based on performance, hazard, and economic considerations , 2018, Nature Nanotechnology.

[154]  R. M. Rivero,et al.  Abiotic and biotic stress combinations. , 2014, The New phytologist.

[155]  Yanpeng Wang,et al.  CRISPR/Cas Genome Editing and Precision Plant Breeding in Agriculture. , 2019, Annual review of plant biology.

[156]  M. Sillanpää,et al.  Dynamic Quantitative Trait Locus Analysis of Plant Phenomic Data. , 2015, Trends in plant science.

[157]  Petr Havlik,et al.  Comparing impacts of climate change and mitigation on global agriculture by 2050 , 2018, Environmental Research Letters.