Systems Modeling PDF
Preview Systems Modeling
Mukhtar Ahmed Editor Systems Modeling Systems Modeling Mukhtar Ahmed Editor Systems Modeling Editor MukhtarAhmed DepartmentofAgriculturalResearchfor NorthernSweden SwedishUniversityof AgriculturalSciences Umeå,Sweden DepartmentofAgronomy PirMehrAliShahAridAgricultureUniversity Rawalpindi,Pakistan ISBN978-981-15-4727-0 ISBN978-981-15-4728-7 (eBook) https://doi.org/10.1007/978-981-15-4728-7 #SpringerNatureSingaporePteLtd.2020 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe materialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. 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The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface Artificial intelligence (AI) or machine intelligence is helping mankind to solve differentproblemsatafasterpace.Similarly,qualitativeandquantitativeknowledge isincreasingatarapidpacewiththeinventionofmoderntools.Thesetoolshelpto generate big data sets that can be used by different decision support tools. Knowl- edgeismeagreandunsatisfactory ifit isnotinthenumericalform.Thus,artificial intelligenceisplayingaroletogeneratebigdatasetsinnumericalform.Sustainable agriculturalproductionrequiresnewmethodsandtechniquesunderchallengeslike climate change, market globalization, and increased population. Field-based approaches (e.g., agronomic diagnosis and prototyping) have been used success- fully, but these approaches are too slow to provide timely responses to such rapid contextual changes. Similarly, a large number of systems could not be easily explored by using such techniques. Current social, political, and environmental concernscouldbeeasilytackledbytheuseofinsilicoapproaches.Theseapproaches canhelpstudyabroaderrangeofpossiblesystemsthroughmodelingandsimulation, and can offer the possibility of identifying more quickly new sustainable systems. The goals of agroecosystems models can be sorted into the following groups: (i) models as representative of knowledge, concepts, and methods for scientists, (ii) modelsascommunicationtools,(iii)models astoolstomanageorrunsystems (iv), models as tools to assist debates, and (v) models to design crop management systems. Models have been used as an excellent tool to develop new cropping systems. Steps to design new cropping systems include (i) defining goals and constraintsofnewcroppingsystems,(ii)designingofnewcompatiblesystemswith thesetofconstraints,(iii)evaluationofnewsystems,and(iv)testingandtransferof newinnovativesystemstothepractitioners.Simulationmodelscanbeinstrumental in determining recommendations for various agro-technology packages. Crop modelshelpustounderstandcomplexandnonlinearcropresponsestomanagement atdifferentspatio-temporalscales(e.g.,differentsoilandclimate).Similarly,innu- merableinteractionsamongweather,soil,crop,andmanagementfactorsthroughout thegrowingseasoncouldbeeasilyexploredthroughmodeling.Modelscanpredict cropproductivityundervariousclimatechangescenariosthatareevennotpossible through field experimentation. Simulated outputs can be delivered to the policymakers at local, national, regional, and global levels to help implement appropriate measures. Computer applications in the field of agriculture can help to v vi Preface understandtheinteractionsbetweenthesystemanditsvariables.Models,whichare mainlymathematicalrepresentationsofthebiologicalsystem,cangenerateanswers to the problems. Most people think that models are complicated and complex thus needtimetobeimplementedonthegroundscale.However,nospecialmathematics are necessary for big or complex models. They come from small bits and pieces. Thereisaprosperousfutureforsystemsmodeling,anditcanopennewfrontiers,and it helps in the agroecological transitions of agriculture. Similarly, it’s essential to understandbelowgroundprocesses,roots,soil,andtheircomplexabioticandbiotic interactions.Weneedtoconsiderplantsorcropsasholobionts(individualhostand its microbial community). Such consideration can account for their extended phenotypesand(phyllosphereandrhizosphere)microbiomes.Simulationisagood substitute for experiments, and it has been shown by different researchers and technologists that modelswork with a higher degree ofaccuracy. Thus, we should include simulation at all levels of system understandings. The system can be soil, plant,andatmosphere.Thisbookwithtitle“SystemModeling”isusefulforunder- graduate and post-graduate students from different disciplines of Data Science, Agronomy, Crop Physiology, Plant Breeding, Plant Pathology, Entomology, Soil Science,RemoteSensing,AgriculturalMeteorology,andEnvironmentalScience.It can be used by policymakers and administrators to direct teaching, research, and extensionactivities. Chapter1presentsafundamentaldescriptionofSystemsModelinginwhichthe focus isagricultural systems that have complex interactionswiththeirsurrounding environments and soil, and in which a better understanding is possible through computer applications. Solar radiation, temperature, photoperiod, humidity, ozone, andwindaresomeoftheimportantenvironmentalvariableswhichinteractwiththe agriculturalsystemthatarediscussedinthischapter.Thesevariablesareimportant considerationsforthedevelopmentofunderstandingoftheagriculturalsystemona scientific basis. Similarly, the application of different models at different scales is presented, which could help one to understand the mechanisms in qualitative and quantitative ways. Finally, the concept of digital agriculture and its linkage with modelingiselaborated.Ingeneral,thechapterdiscussesindetailthetype,methods of measurement along with mathematical representation, terminologies and their impactsonthevariousprocessesofplants.Chapter2summarizescropphenotyping and elaborates on different techniques/approaches used in the process of phenotyping. Corresponding to genotypic, the phenotypic form of the plant is more important for high yield. The selection of germplasm based on phenotype has been of great interest of breeders and farmers. Considering the importance of phenotyping,Tuberosa(2012)referredtophenotypingas“king”andheritabilityas “queen.” Chapter 3 discusses the role of statistics and modeling for the analysisof experimentaldata.Alsodiscussedisthedatathatshouldbecollectedtoaddressour researchquestionsandwhatshouldbeourexperimentaldesign.Alltheseaspectsare discussedinthischapterwiththedescriptionaboutCompletelyRandomizedDesign (CRD),RandomizedCompleteBlockDesign(RCBD),LatinSquareDesign,Nested andSplitPlotDesign,Strip-Plot/Split-BlockDesign,Split-SplitplotDesign,facto- rial experiments, fractional factorial design, multivariate analysis of variance (MANOVA), Analysis of Covariance (ANCOVA), Principal component analysis, Preface vii regression,correlation,anddifferentanalyticaltools/softwares.Chapter4focuseson differentdynamicmodelingapproachesanddescriptionofdifferentdynamicmodels in practical use. Similarly, a general description of modeling with a history of dynamicmodelingfromtheeighteenthcenturyuntiltodayispresented.Calibration ofcropmodelasstandardpracticeandtheestimationofcropparametersbasedupon observedfielddataarediscussedinChap.5. Calibrationistheprocessoftheestimationofunknownparametersusingpracti- calobservations.Itisgenerallycarriedoutmanuallybyadjustingthesettingsofthe model.Itconsistsofchoosingtheaccuratecoefficientsthatplayasignificantrolein the adjustment of soil nitrogen, soil organic carbon, soil phosphorus, crop growth, phenological development, biomass accumulation, dry matter partitioning, nutrient uptake,graindryweight,grainnumbers,grainyield,grainnitrogen(N)atmaturity, and protein contents. Chapter 6 presents the application of crop models for wheat production. Potential and limitation of wheat crop models to assist breeders, researchers, agronomists, and decision-makers are discussed in this chapter. Chapter 7 is about genetic analysis that requires phenotyping and genotyping, followedbytheapplicationofstatisticalprinciples.Chapter8elaboratesthecontri- bution of process-based models in sugarcane research. Climate characterization of the leading sugarcane producing countries with the influence of main weather variables on sugarcane growth, development, and yields are presented in this chapter. Chapter 9 presents the forecasting of rainfed wheat yield using Landsat 8 satellite imagery and DSSAT. Methane (CH ) is a potent greenhouse gas that is 4 producedinmanysectors,andisdiscussedinChap.10.Measurementsofmethane are impossible in some cases, thus in vitro techniques together with modeling approaches are presented in this chapter to predict methane emissions. Chapter 11 isareviewofsunflowermodelingwithadescriptionofdifferentmodelsusedinthe improvement of sunflower. Disease modeling is discussed in Chap. 12. DSSAT- CROPGRO-ChickpeamodelispresentedinChap.13. Chapter14focusesonpotatoes,whichisoneoftheimportantcropsintheworld after rice and wheat. This crop is under threat due to climate variability; thus, different adaptation strategies are needed through simulation modeling to mitigate the impacts of climate change. Different process-based models such as Decision Support System for Agrotechnology Transfer (DSSAT), Agricultural Production Systems Simulator (APSIM), CropSyst (CropSyst VB – Simpotato), and STICS (Simulateur multidisciplinaire pour les Cultures Standard) are presented in this chapter as they have shown great potential to develop sustainable agronomic practices as well as virtual potato cultivars to have good potato cultivars for the future.Finally,inChap.15,applicationofageneralizedadditivemodelforrainfall forecasting is presented with the aim to predict the most suitable sowing time for rainfedwheat. Itismyhopethatknowledgeaboutsystemmodelingpresentedinthisbookwill enhance the understanding and catalyze the application of artificial intelligence, phenotyping,andmodelingatdifferentscales. Rawalpindi,Pakistan MukhtarAhmed Contents 1 SystemsModeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 MukhtarAhmedandShakeelAhmad 2 CropPhenotyping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 MuhammadTariq,MukhtarAhmed,PakeezaIqbal,ZartashFatima, andShakeelAhmad 3 StatisticsandModeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 MukhtarAhmed 4 DynamicModeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 MukhtarAhmed,MuhammadAliRaza,andTaimoorHussain 5 ModelsCalibrationandEvaluation. . . . . . . . . . . . . . . . . . . . . . . . . 151 MukhtarAhmed,ShakeelAhmad,MuhammadAliRaza, UttamKumar,MuhammadAnsar,GhulamAbbasShah, DavidParsons,GerritHoogenboom,TaruPalosuo,andSabineSeidel 6 WheatCropModellingforHigherProduction. . . . . . . . . . . . . . . . 179 AhmedMohammedSaadKheir,ZheliDing, MarwaGamalMohamedAli,TilFeike,AlyIsmailNagibAbdelaal, andAbdelrazekElnashar 7 GeneticAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 MunirAhmadandRashidMehmoodRana 8 Sugarcane:ContributionofProcess-BasedModelsfor UnderstandingandMitigatingImpactsofClimateVariability andChangeonProduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 HenriqueBorioloDiasandGeoffInman-Bamber 9 ForecastingofRainfedWheatYieldinPothwarUsingLandsat 8SatelliteImageryandDSSAT. . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 SanaYounas,MukhtarAhmed,andNaeemAbbasMalik ix x Contents 10 MethaneProductioninDairyCows,Inhibition,Measurement, andPredictingModels. . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. 295 MohammadRamin,JuanaC.Chagas,andSophieJ.Krizsan 11 SunflowerModelling:AReview. . . . . . . . . . . . . . . . . . . . . . . . . . . 307 AdnanArshad,MuhammadUsmanGhani,MahmoodulHassan, HumaQamar,andMuhammadZubair 12 DiseaseModelingasaTooltoAssesstheImpactsofClimate VariabilityonPlantDiseasesandHealth. . . . . . . . . . . . . . . . . . . . . 327 MuhammadZeeshanMehmood,ObaidAfzal, MuhammadAqeelAslam,HasanRiaz,MuhammadAliRaza, ShakeelAhmed,GhulamQadir,MukhtarAhmad,FaridAsifShaheen, Fayyaz-ul-Hassan,andZahidHussainShah 13 ChickpeaModelingUnderRainfedConditions. . . . . . . . . . . . . . . . 353 AfifaJavaid,MukhtarAhmed,Fayyaz-ul-Hassan, Mahmood-ul-Hassan,MunirAhmad,andRifatHayat 14 PotatoModeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 MukhtarAhmed,ZartashFatima,PakeezaIqbal,ThairaKalsoom, KashifSarfrazAbbasi,FaridAsifShaheen,andShakeelAhmad 15 ApplicationofGeneralizedAdditiveModelforRainfall ForecastinginRainfedPothwar,Pakistan. . . . . . . . . . . . . . . . . . . . 403 MukhtarAhmed,Fayyaz-ul-Hassan,ShakeelAhmad,RifatHayat, andMuhammadAliRaza Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 About the Editor Mukhtar Ahmed’s research focuses on the impact of climatechangeoncropecology,cropphysiology,crop- ping system and rain-fed ecosystem management. He hasbeeninvolvedinteachingandresearchsince2005. During his PhD and visit to Sydney University, Australia,heworkedontheapplicationofAPSIMasa decision support tool, and rainfall forecasting using generalised additive models. He was awarded a young scientist fellowship by APCC South Korea. He also wonaresearchproductivityawardfromPakistanCoun- cil of Science and Technology (PCST), and a Publons reviewer award in 2018 and 2019. He was part of the Regional Approaches for Climate Change (REACCH) project in the USA, which developed multi-model ensemble approaches to minimize the uncertainties. He is involved in the use of statistical and dynamic models as risk management tools to mitigate the challenges of climate change. His current research includes agroecosystems modelling, precision agricul- ture, modelling the nutrient use efficiency of legume- based cropping systems, forage agronomy and physio- logical responses to climate variability and its modelling. He is a Project co-leader in the Model Calibration Group of the Agricultural Model Intercom- parison and Improvement Project(AGMIP) Wheat and MaizeEvapotranspiration. xi
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