This Research Topic aims to showcase research, development and technology (RDT) work toward devising and delivering integrated solutions that support and enhance the climate smart landscape (CSL)-based approach. This Research Topic comprises 13 articles, including 10 Original Research articles,1 Review, 1 Hypothesis and Theory article, and 1 Technology Report. State-of-the-art modeling approaches and sampling technologies are showcased. Contributed papers present new methodological/technological innovation, findings, and/or insights across four themes: (1) landscape productivity and crop suitability, (2) variable crop requirements for water and nutrients,(3) crop health status, phenology and phenotyping, (4)crop disease assessment and prediction under integrated pest management (IPM) and the CSL approach

Guo, W

Huete, A

Kouadio, L

Newlands, NK

Porcelli, TA

Potgieter, AB

Frontiers in Environmental Science

English

OPUS - University of Technology Sydney

Editorial: Building and delivering real-world, integrated sustainability solutions: Insights, methods and case-study applications

Nathaniel K. Newlands

Tracy A. Porcelli

Andries B. Potgieter

Louis Kouadio

Alfredo Huete

Wei Guo

Directory of Open Access Journals

EDITORIALpublished: 28 May 2019doi: 10.3389/fenvs.2019.00071Frontiers in Environmental Science | www.frontiersin.org 1 May 2019 | Volume 7 | Article 71Edited and reviewed by:Hayley Jane Fowler,Newcastle University, United Kingdom*Correspondence:Nathaniel K. Newlandsnathaniel.newlands@canada.caSpecialty section:This article was submitted toInterdisciplinary Climate Studies,a section of the journalFrontiers in Environmental ScienceReceived: 12 April 2019Accepted: 10 May 2019Published: 28 May 2019Citation:Newlands NK, Porcelli TA,Potgieter AB, Kouadio L, Huete A andGuo W (2019) Editorial: Building andDelivering Real-World, IntegratedSustainability Solutions: Insights,Methods and Case-StudyApplications. Front. Environ. Sci. 7:71.doi: 10.3389/fenvs.2019.00071Editorial: Building and DeliveringReal-World, Integrated SustainabilitySolutions: Insights, Methods andCase-Study ApplicationsNathaniel K. Newlands 1*, Tracy A. Porcelli 2, Andries B. Potgieter 3, Louis Kouadio 4,Alfredo Huete 5 and Wei Guo 61 Agriculture and Agri-Food Canada, Science and Technology Branch, Summerland Research and Development Centre,Summerland, BC, Canada, 2Newelli Consulting, Kelowna, BC, Canada, 3Queensland Alliance for Agriculture and FoodInnovation, University of Queensland, Toowoomba, QLD, Australia, 4 International Centre for Applied Climate Sciences,University of Southern Queensland, Toowoomba, QLD, Australia, 5 School of Life Science, University of Technology Sydney,Ultimo, NSW, Australia, 6 Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and LifeSciences, The University of Tokyo, Nishitokyo, JapanKeywords: adaptation, agriculture, big data, real-world, sustainabilityEditorial on the Research TopicBuilding and Delivering Real-World, Integrated Sustainability Solutions: Insights, Methodsand Case-Study ApplicationsSustaining ecosystems to deliver what people need and value, while mitigating and adaptingto global climate change and extreme event impacts, presents a complex set of environmental,economic, and social challenges in ensuring resilient and sustainable food production (Fuhrer,2007; Newlands, 2016; Whitfield et al., 2018). Regional climate trends drive variability in landproductivity, soil water and nutrient availability, crop calendars, and the prevalence of pests andpathogens. Abrupt extreme events that defy clear prediction and attribution to climate trends causecatastrophic crop damage through water-logging, soil erosion, nutrient leaching, heat waves anddrought. Sayer et al. (2013) proposed 10 core principles for an integrated landscape approach forreconciling agriculture, conservation, and other competing land uses. Building on these principles,the Climate Smart Landscape (CSL) approach has emerged as an integrated management strategyto address the increasing pressures on agricultural production, ecosystem conservation, rurallivelihoods, and climate change mitigation/adaptation (Scheer et al., 2012; Salvini et al., 2018).The CSL approach is strengthened by a broad array of different science-based indicators,metrics, frameworks and modeling systems that enhance its capability to make more informed,integrated decisions. This approach, however, needs to incorporate big data, statistical, and artificialintelligence (AI) methodological improvements, new sensors, and remote-sensing technologicaladvancements (Lee et al., 2010; Wolfert et al., 2017; Willcock et al., 2018). Such improvementsoffer more flexible monitoring, newer and/or higher-resolution data, better prediction methods,and tools for decision support.This Research Topic aims to showcase research, development and technology (RDT) worktoward devising and delivering integrated solutions that support and enhance the CSL-basedapproach. This Research Topic comprises 13 articles, including 10 Original Research articles,1 Review, 1 Hypothesis and Theory article, and 1 Technology Report. State-of-the-artmodeling approaches and sampling technologies are showcased. Contributed papers presentnew methodological/technological innovation, findings, and/or insights across four themes: (1)landscape productivity and crop suitability, (2) variable crop requirements for water and nutrients,Newlands et al. Integrated Sustainability Solutions(3) crop health status, phenology and phenotyping, (4)crop disease assessment and prediction under integrated pestmanagement (IPM) and the CSL approach.LANDSCAPE PRODUCTIVITY AND CROPSUITABILITYBock et al. provide a technology report on a Canadian LandSuitability Rating System (LSRS) is a rule-based algorithmintegrating soil, climate, and landscape factors from accessibledatabases, calculating a classed suitability rating for a givenlandscape. It is used to support commercial field crop production,and as a spatial research tool for assessing climate changeimpacts. Subramanian and Crowley present a spatial-basedreinforcement learning approach that uses satellite imagery toincrease the predictive power of spatial dynamic models thatpredict, and learn better policies to manage and control spatiallyspreading processes. They apply this methodology in wildfireevent prediction using Canadian data. Exploring landscapegradients, Xu et al. assess how measuring plant communityassemblies along habitat severity gradients may improve ourability to understand and monitor community dynamics andspecies responses under future climate change.VARIABLE CROP REQUIREMENTS FORWATER AND NUTRIENTSNeilsen et al. showcase a landscape-based water demand modelfor agricultural water use that regional water managers canuse to better manage water demand and supply in responseto climate change. This model considers high-resolution landuse, soil, elevation, historical/future climate scenario data, adigital elevation model, sub-basins, aquifers, and socio-politicaljurisdictional boundaries. They use this model to explore futurescenarios of climate change, and historical effects of cropproduction systems on irrigation water demand. From water tonutrient requirements, Guo et al. investigate aerially estimatingnitrogen update frommulti-angular hyperspectral data on winterwheat to improve the efficiency of remotely-based techniques fornon-destructive, rapid detection of wheat nitrogen (N) nutrientstatus. A novel, modified right-side peak area index (mRPA)is benchmarked against other widely used indices, and shownto have the highest predictive power. Using this index canincrease accuracy in assessing crop N status and management.Martins et al. propose a novel methodology for tracking cropmicronutrient composition over time and demonstrate it forpredicting maize/corn maximum requirements under variationin nutrient uptake rates, potential evapotranspiration, andmicronutrient partitioning over crop growth stages.CROP HEALTH STATUS, PHENOLOGY,AND PHENOTYPINGWatson et al. use time-lapsed “phenocam” cameras to trackthe phenology and identify phenological variability of nativeand exotic grasses across grassland areas in Australia. Theirfindings indicate C3/C4 species dominance to be the primarydriver of phenological differences among grassland types,with the proportion of non-photosynthetic vegetation, grazingpressure, and species-dependent responses to rainfall andtemperature being important biophysical drivers of grasslandphenology. MODIS/Landsat satellite and field-based phenocamdata were found to be in good agreement. A primary benefitof phenocam data is its higher temporal fidelity in capturingvegetation changes (i.e., increases/decreases in greenness) overperiods of only 5 days, compared to coarser satellite orfield measurement techniques. Using high-resolution imagescaptured by unmanned aerial vehicles (UAVs), Guo et al.propose a two-step machine-learning based image processingmethod that can provide more reliable estimates of yield bydetecting and counting the number of heads, than manualmeasurements in sorghum breeding trials, with potential forbroader application in field experiments, and field productionscouting operations. To better assess adaptation traits in largesmall-plot breeding trials, Potgieter et al. evaluate the use ofa narrowband multi-spectral camera deployed on a UAV. LeafArea Index (LAI), Normalized Difference Vegetation Index(NDVI), Enhanced Vegetation Index (EVI), and NormalizedDifference Red Edge (NDRE) were evaluated. Despite variableemergence, these indices tracked canopy cover and LAI wellover a large range of plant densities, with NDVI and EVIstrongly correlated with plant number per plot, canopy cover,and LAI. NDRE (i.e., leaf chlorophyll content) was foundto be most useful in characterizing the leaf area dynamicsand senescence patterns in contrasting genotypes. In furtheraddressing practical constraints in genomics-assisted breeding,Watanabe et al. demonstrate UAV remote sensing (with a RGBor near-infrared green and blue (NIR-GB) camera) for measuringsorghum plant height and nitrogen availability for faster andmore cost-effective throughput phenotyping. For phenotypingbased on root depth distributions, Wasson et al. propose astate-of-the-art Bayesian hierarchical’ nonlinear mixed statisticalmodeling approach to estimate root depth distributions forwheat genotypes to enable breeders to select for whole rootsystem distributions appropriate for sustainable intensification.This approach produces de-noised profiles that exhibit rigorouslydiscernible phenotypic traits.CROP DISEASE ASSESSMENT,PREDICTION UNDER IPM AND CSLAPPROACHPandey et al. provide a review of soil-borne and foliarfungal diseases of mungbean (Vigna radiata var. radiata),an important legume crop in South/Southeast Asia. Theyreview pathogen characterization, economic impacts, andintegrated management practices including host resistance,fungicides, biocontrol agents, natural plant products, and culturalpractices. They highlight the need for longer-term studies tovalidate biological methods for commercial application. Forwheat (yellow) stripe rust (Puccinia striiformis f.sp. tritici)fungal disease, the greatest global pathogen threat to wheatFrontiers in Environmental Science | www.frontiersin.org 2 May 2019 | Volume 7 | Article 71Newlands et al. Integrated Sustainability Solutionsproduction worldwide, Newlands explores the feasibility ofan integrated model-based framework for predicting andcontrolling across large agricultural regions, using a novelspatially-explicit complex model, climate reanalysis and weatherstation network data.Deploying cheaper, more accurate, and efficienttechnology enables the harnessing big data for use insolving sustainability challenges. With improved integratedanalytical frameworks, statistical approaches, spatially-explicit models and indices, the CSL approach can befurther developed and applied for more resilient, productive,and sustainable ecosystems. Smarter models will howeverrequire sufficient training data and operational frameworksassimilating data across a broad range of sampling platformsand data types. Agri-business will likely play an increasingrole in sustainable landscape management by involvingstakeholders and monitoring progress and outcomes (Salviniet al., 2018). Collectively, we can move faster to confrontsuch complex interplay involved in translating scientificevidence into real-world operational or actionable solutions(Lamontagne et al., 2019).AUTHOR CONTRIBUTIONSNN and TP co-wrote this editorial based on contributions to theResearch Topic, incorporating editorial feedback provided by AP,LK, AH, and WG.ACKNOWLEDGMENTSWe thank all authors of contributions to this ResearchTopic for preparing high-quality manuscripts undera tight time schedule and the reviewers for criticallyevaluating the papers and for further improving themwith their questions, comments, and suggestions. NKNacknowledges research funding support of the CanadianAgricultural Partnership (CAP), Agriculture and Agri-FoodCanada (AAFC).REFERENCESFuhrer, J. (2007). “Chapter 7: Sustainability of crop production systems underclimate change,” in Agroecosystems in a Changing Climate, eds P. C. D. Newton,R. A. Carran, G. R. Edwards, and P. A. Niklaus (Boca Raton, FL: Taylor andFrancis; Chapman and Hall; CRC Advances in Agroecology Series), 167–185.Lamontagne, J. R., Reed, P. M., Marangoni, G., and Garner, G. G. (2019).Robust abatement pathways to tolerable climate futures require immediateglobal action. Nat. Clim. Chan. 9, 290–294. doi: 10.1038/s41558-019-0426-8Lee,W. S., Alchanatis,V.,Yang,C., Hirafuji, M., Moshou, D., and Li, C. (2010).Sensing technologies for precision specialty crop production.Comput. Electron.Agric. 74, 2–33. doi: 10.1016/j.compag.2010.08.005Newlands, N. K. (2016). Future Sustainable Ecosystems: Complexity, Risk,Uncertainty. Boca Raton, FL: Taylor and Francis (Chapman and Hall/CRCApplied Environmental Statistics Series).Salvini, G., Dentoni, D., Ligtenberg, A., Herold, M., and Bregt, A. K. (2018).Roles and drivers of agribusiness shaping Climate-Smart landscapes: a review.Sustain. Dev. 26, 533–543. doi: 10.1002/sd.1897Sayer, J., Sunderland, T., Ghazoul, J., Pfund, J. L., Sheil, D., Meijaard, E., et al.(2013). Ten principles for a landscape approach to reconciling agriculture,conservation, and other competing land uses. Proc. Nat. Acad. Sci. U S A. 110,8349–8356. doi: 10.1073/pnas.1210595110Scheer, S. J., Shames, S., and Friedman, R. (2012). From climate-smartagriculture to climate-smart landscapes. Agric. Food Secur. 1:12.doi: 10.1186/2048-7010-1-12Whitfield, S., Challinor, A. J., and Rees, R. M. (2018). Frontiers in climate smartfood systems: outlining the research space. Front. Sustain. Food Syst. 2:2.doi: 10.3389/fsufs.2018.00002Willcock, S., Martinez-López, J., Hooftman, D. A. P., Bagstad, K. J., Balbi, S.,Marzo, A., et al. (2018). Machine learning for ecosystem services. Ecosyst. Serv.33, 164–174. doi: 10.1016/j.ecoser.2018.04.004Wolfert, S., Ge, L., Verdouw, C., and Bogaardt, M.-J. (2017). Big data insmart farming – a review. Agric. Syst. 153, 69–80. doi: 10.1016/j.agsy.2017.01.023Conflict of Interest Statement: The authors declare that the research wasconducted in the absence of any commercial or financial relationships that couldbe construed as a potential conflict of interest.Copyright © 2015 Her Majesty the Queen in Right of Canada. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided theoriginal author(s) or licensor are credited and that the original publication in thisjournal is cited, in accordance with accepted academic practice. No use, distributionor reproduction is permitted which does not comply with these terms.Frontiers in Environmental Science | www.frontiersin.org 3 May 2019 | Volume 7 | Article 71

Editorial: Building and Delivering Real-World, Integrated Sustainability Solutions: Insights, Methods and Case-Study Applications

Newlands, Nathaniel K.

Porcelli, Tracy A.

Potgieter, Andries B.

Kouadio, Louis

Huete, Alfredo

Guo, Wei

University of Southern Queensland ePrints

Editorial: building and delivering real-world, integrated sustainability solutions: insights, methods and case-study applications

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Editorial: Building and delivering real-world, integrated sustainability solutions: Insights, methods and case-study applications

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