CROP GROWTH MODELING AND ITS …

1 CROP GROWTH MODELING AND ITSAPPLICATIONS IN AGRICULTURALMETEOROLOGYV. Radha Krishna MurthyDepartment of Agronomy, College of AgricultureANGR agricultural University, Rajendranagar, HyderabadAbstract: This paper discusses various crop GROWTH MODELING approaches , Mechanistic, Deterministic, Stochastic, Dynamic, Static and Simulationetc. Role of climate change in crop MODELING and applications of crop growthmodels in agricultural meteorology are also discussed. A few successfully usedcrop GROWTH models in agrometeorology are discussed in is defined as an Aggregation of individual plant species grown in aunit area for economic purpose . GROWTH is defined as an Irreversible increase in size and volume and isthe consequence of differentiation and distribution occurring in the plant .Simulation is defined as Reproducing the essence of a system withoutreproducing the system itself.

2 In simulation the essential characteristics ofthe system are reproduced in a model, which is then studied in an abbreviatedtime model is a schematic representation of the conception of a system or anact of mimicry or a set of equations, which represents the behaviour of a , a model is A representation of an object, system or idea in some formother than that of the entity itself . Its purpose is usually to aid in explaining,understanding or improving performance of a system. A model is, by definitionSatellite Remote Sensing and GIS applications in agricultural Meteorologypp. 235-261236 Crop GROWTH MODELING and its applications in agricultural meteorology A simplified version of a part of reality, not a one to one copy . Thissimplification makes models useful because it offers a comprehensivedescription of a problem situation. However, the simplification is, at the sametime, the greatest drawback of the process.

3 It is a difficult task to produce acomprehensible, operational representation of a part of reality, which graspsthe essential elements and mechanisms of that real world system and evenmore demanding, when the complex systems encountered in environmentalmanagement (Murthy, 2002).The Earth s land resources are finite, whereas the number of people thatthe land must support continues to grow rapidly. This creates a major problemfor agriculture. The production (productivity) must be increased to meetrapidly growing demands while natural resources must be protected. Newagricultural research is needed to supply information to farmers, policy makersand other decision makers on how to accomplish sustainable agriculture overthe wide variations in climate around the world. In this direction explanationand prediction of GROWTH of managed and natural ecosystems in response toclimate and soil-related factors are increasingly important as objectives ofscience.

4 Quantitative prediction of complex systems, however, depends onintegrating information through levels of organization, and the principalapproach for that is through the construction of statistical and simulationmodels. Simulation of system s use and balance of carbon, beginning with theinput of carbon from canopy assimilation forms the essential core of mostsimulations that deal with the GROWTH of are webs or cycles of interacting components. Change in onecomponent of a system produces changes in other components because of theinteractions. For example, a change in weather to warm and humid may leadto the more rapid development of a plant disease, a loss in yield of a crop,and consequent financial adversity for individual farmers and so for the peopleof a region. Most natural systems are complex. Many do not have bio-system is comprised of a complex interaction among the soil, theatmosphere, and the plants that live in it.

5 A chance alteration of one elementmay yield both desirable and undesirable consequences. Minimizing theundesirable, while reaching the desired end result is the principle aim of theagrometerologist. In any engineering work related to agricultural meteorologythe use of mathematical MODELING is essential. Of the different modelingtechniques, mathematical MODELING enables one to predict the behaviour ofdesign while keeping the expense at a minimum. agricultural systems arebasically modified ecosystems. Managing these systems is very difficult. TheseV. Radha Krishna Murthy237systems are influenced by the weather both in length and breadth. So, thesehave to be managed through systems models which are possible only throughclassical engineering OF MODELSD epending upon the purpose for which it is designed the models areclassified into different groups or types.

6 Of them a few are :a. Statistical models: These models express the relationship between yieldor yield components and weather parameters. In these models relationshipsare measured in a system using statistical techniques (Table 1).Example: Step down regressions, correlation, Mechanistic models: These models explain not only the relationshipbetween weather parameters and yield, but also the mechanism of thesemodels (explains the relationship of influencing dependent variables). Thesemodels are based on physical Deterministic models: These models estimate the exact value of the yieldor dependent variable. These models also have defined Stochastic models: A probability element is attached to each output. Foreach set of inputs different outputs are given alongwith probabilities. Thesemodels define yield or state of dependent variable at a given Dynamic models: Time is included as a variable.

7 Both dependent andindependent variables are having values which remain constant over a givenperiod of Static: Time is not included as a variables. Dependent and independentvariables having values remain constant over a given period of Simulation models: Computer models, in general, are a mathematicalrepresentation of a real world system. One of the main goals of cropsimulation models is to estimate agricultural production as a function ofweather and soil conditions as well as crop management. These modelsuse one or more sets of differential equations, and calculate both rate andstate variables over time, normally from planting until harvest maturityor final GROWTH MODELING and its applications in agricultural MeteorologyTable 1. Prediction models for crop GROWTH , yield components and seed yieldof soybean genotypes with meteorological observations GENOTYPEMACS-201 MACS-58 Plant + MAT1 + MIT1 MIT3 + MT3 HTU3 R2 = HTU3 R2 = + SS1 + RH21 + MAT2per MAT3 R2 = MAT3 R2 = MT1 RH11 + MIT2 GDD2 RH13 + RH12 SS3+ MT3 GDD3 R2 = = SS1 RH11 SS1 -(dm2 m-2)

8 SS2 + HTU2 MIT2 + RH22 + RH13 RH23 R2 = GDD3 R2 = area + SS1 RH11 RH21+ MAT3 + RH23 + HTU1 + HTU2 HTU3 R2 = MAT3 R2 = + RH11 + RH11 HTU1 weight+ HTU2 + MAT3 RH12 MAT3+ SS3 HTU3R2 = = of + MIT1 + + HTU3per plant+ SS1 + MAT3 + RH23R2 = + SS3 HTU3 R2 = per + MAT1 + MIT1 GDD1+ MIT2 RH13 + GDD3 R2 = + SS3 GDD3R2 = seed RH11 GDD2 + RH11 RH21MT3 GDD3 R2 = HTU1 RH22 + + MT3 GDD3R2 = + RH11 RH11 RH21+ MAT2 HTU2 HTU1 MAT2R2 = + HTU2 MAT3 R2 = RH21 RH11 MAT3 R2 = HTU3 R2 = + SS1 RH21R2 = + HTU1 RH12 HTU2 SS3 R2 = Radha Krishna RH12 + MT2 RH12 RH22per plantMAT3 R2 = MAT3 R2 = + MAT1 RH21 + HTU1 MIT2 MIT2 + RH12 = + GDD2 SS3 GDD3R2 = RH11 + RH21 + HTU1(dm2 m-2)

9 SS1 MAT2 + RH12 RH22 + + MT3 GDD3 HTU2 + MIT3 RH13R2 = SS3 GDD3R2 = area RH21 SS1 RH22 HTU3 HTU1 MAT2 + HTU2R2 = MAT3 + RH23 + SS3R2 = + MIT1 MAT1 HTU1 MAT2 + HTU1 + MAT2 + + HTU2 + SS3 HTU3MT2 + GDD2 + HTU2R2 = + RH13 SS3 R2 = of RH21 + MAT1 + SS1per plant+ HTU2 MAT3+ MT2 MIT2 R2 = RH12 HTU3 R2 = per HTU1 MIT + RH11+ HTU2 MAT3+ GDD1 + MAT2 = HTU2 RH13 + RH23R2 = seed + MAT2 MAT1 RH11R2 = RH21 + MAT2 + RH23 SS3 GDD3 + HTU3R2 = + SS1 + MAT1 RH21R2 = HTU1 RH22 GDD2 GDD3 R2 = MIT1 = HTU1 RH12 HTU2 MAT3 R2 = GENOTYPEMACS-201 MACS-58240 Crop GROWTH MODELING and its applications in agricultural MeteorologyGENOTYPE -MACS-330 Plant MAT1 + SS1 MAT2 RH12 + RH22 + RH13 + MT3 HTU3 R2 = per MT1 + GDD1 + MT2 GDD2 MIT3R2 = RH21 + SS2 HTU2 R2 = area (dm2 m2)

10 RH11 RH12 + MT2 GDD2 HTU2+ MAT3 RH13 HTU3 R2 = area + RH21 + SS1 GDD1 + RH22 + SS2+ MAT3 + RH13 SS3 GDD3 R2 = dry MAT1 RH11 + RH21 RH12 + RH22+ SS2 HTU2 SS3 + HTU3 R2 = of pods per MIT1 + RH11 + SS1 MAT2 + HTU2 + SS3 HTU3 R2 = per SS1 MAT2 RH12 + MAT3 RH13 R2 = seed RH11 + RH21 + SS1 + MAT2 + RH22 + RH13 MT3 HTU3 R2 = RH11 + RH21 MT1 + MAT2 RH12 + GDD2 + MAT3 HTU3 R2 = + RH23 HTU3 R2 = Maximum temperature in phase 1SS1 Sunshine hours in phase 1 MAT2 Maximum temperature in phase 2SS2 Sunshine hours in phase 2 MAT3 Maximum temperature in phase 3SS3 Sunshine hours in phase 3 MIT1 Minimum temperature in phase 1 GDD1 Growing degree days in phase 1 MIT2 Minimum temperature in phase 2 GDD2 Growing degree days in phase 2 MIT3 Minimum temperature in phase 3 GDD3 Growing degree days in phase 3MT1 Mean temperature in phase 1 HTU1 Heliothermal units in phase 1MT2 Mean temperature in phase 2 HTU2 Heliothermal units in phase 2MT3 Mean temperature in phase 3 HTU3 Heliothermal units in phase 3RH11 Relative humidity in the morning in phase 1RH12 Relative humidity in the morning in phase 2RH13 Relative humidity in the morning in phase 3RH21 Relative humidity in the evening in phase 1RH22 Relative humidity in the evening in phase 2RH23 Relative humidity in the evening in phase 3V.