Land-Use Management - Science topic

As I assert in my book, Fifty Years of Biodynamic Farming (available on ResearchGate), the regenerative techniques described above ultimately are rooted in the holistic, spiritual relationship of the farmer to the local and global environment. The extractive methods practiced in conventional agriculture have brought us to the tipping point of climate disaster, ecological ruin, declining public health, and economic failure.

In ENVI, image differencing and thematic change detection methods are available which are very easy to use. The image differencing method is used for change detection using raster images such as raster indices images.

Thematic change detection method uses classified initial and final mages.

It is reported in different studies that this method provides better accuracy.

Both methods provides detail change statistics.

From a southern perspective, I will go for option 2. Like @Luis Fernando indicated, we need to rethink our understanding of city making especially in the global south. Obviously the city is being shaped largely by the people as opposed to 'our plans' - let's plan from the peoples perspective (inclusive, participatory planning). Miraftab's piece on 'insurgent planning' will offer you a clearer insight of my point of departure. Hope this helps

My Forest and many or all that I know about do not or would not intentionally justify using an invasive exotic plant any more. The change over started to occur about 2 decades ago. We even went so far to discuss and go against state BMPs when the published seed mix included grasses considered invasive like Bahia grass, Serecea lespedeza, tall fescue, etc. In our seed mixes, we have continued to allow for some desired non-native ANNUALs that typically last a year as quick cover, as some of our native grasses are slow to germinate. It’s very expensive to grow plugs and then transplant. Any new forest plans and most if not all written in last 10-20 years are going to prevent use of non-native invasives. Where loblolly pine trees were planted off-site decades ago, they have been or are still being removed to make way for native species. Our timber, road, fire control activities require equipment to be washed to avoid inadvertent spread of invasives. But as mentioned, some areas are very erosive, so rapid cover annuals in the mix like brown top millet are still used as well as weed free straw when needed. Many of the Native seeds established, even sparingly at first, respond well to prescribed fire, a management tool used with some frequency within extensive portions of the SC National Forests. Invasive plants are actively treated, but as suggested, the presence of undesired persistant or invasives such as Bahia grass, Serecea lespedeza, tall fescue and others along roads and old gully treatments and home sites within a mixed ownership landscape creates long term issue where continuing identification and treatments to invasives are expected. Unfortunately, not all agencies, activities, ownerships and nurseries are on the same page as USFS, but I expect and believe the educational emphasis and discussions of concern may have them headed in the right direction through time, but certainly not fast enough. As suggested, unless this issue were to be legislated, it Is voluntary to all but the most conservation oriented. My concern with the pure legislation route would be many of the native grasses that we were familiar with were slow to establish and grow. A few species did respond well such as switchgrass and partridge pea, but erosion control needs a reasonable amount of cover, and using too much seed of annuals or excessive mulch also is bad due to competition, etc.

See my answer to your personal message. Also look at the excel example there and on the previous answer above. Note you have to have the SOLVER ADD-IN activated in EXCEL. The idea is to have an objective (like minimize costs) and constraints.

There is a classical work LAND EVALUATION by Sys et al.We can refer this protocol...

Your other issues.

1. I put in the same "ad hoc" region all the department which have significantly the same level of means and variance in yields.

In my opinion, with 15 years data you have limited scope for the standard tests and models. So you need to focus on the substantive question about farmer behaviour and develop your own methodology. You hypothesize that farmers change their approach to land use based on risk and risk is revealed by variability in yield of maize. (I note that the output from maize in France is both silage and grain but I infer from the context that the data for yield refers to grain.)

Maize as grain has specific climatic and soil needs that may be revealed more by mean yield than variance in yield. So you could try using mean yield to group your departments. You would test the difference in means as a criterion using the F-test and you might impose a distance restriction to avoid disparities in environmental variables. If it is possible to group departments in this way, then you have another method to test Brownian motion. Within a group of departments that appear to form an ad hoc region, what is the pattern of change in yield from year 1 to year 15, trend, random walk, mean reverting? As I understand the theory. Brownian motion would give a random walk. If you get a similar pattern for the departments in an ad hoc region, the there would be a low probability that random walks would have the same profile over time. You could aggregate the data for a ""region" and apply various tests to determine if the patterns differ significantly.

2. Testing the proportions of land in various uses.

Let us say that you have 4 uses. If you attempt to assess all 4 land uses that would mean you have only 3 degrees of freedom. In my opinion, you should begin by testing each land use independently. If you hypothesize that excess risk may drive farmers away from maize as a grain crop, then you are not obliged, at least in the first instance, to say which alternative activity they adopt. Instead, you can break the analysis into two steps.

1. Do farmers shift out of maize-as-grain production in (a) a systematic way over time in groups of nearby or contiguous departments, or (b) does the process appear to be based on the variance in yield and lack of coherence in the pattern of grain yields across departments over time. A shift based on (a) might indicate environmental factors ( We are all in the same boat / Le soleil brille pour tout le monde) , while (b) might indicate pure risk random or without specific cause (Everybody for hinself / Sauve qui peut).

2. When farmers shift away from grain production do they shift to the same suite of alternative productive activities? When they shift away from maize as grain, do they shift to the same activities in the northeast, northwest, central, southeast or southwest. If not, why not?

I am biased by my training as a geographer, In my opinion, the FAO map that I referenced previously, indicates that France is large enough in extent, both latitudinally and longitudinally, that soil, temperature, rainfall, elevation, etc. vary widely. My experiences in Ontario Canada and Ohio and other parts of the American Midwest lead me to think that you may need to use explicitly some weather data for the 15 years of your study.

As I recall maize-as-grain has specific requirements (though hybrid varieties modify the particulars): 200 days growing season, 1000 mm of rainfall per annum concentrated in summer. At the latitude of France, elevation above a couple of hundred meters would be a negative,. The same for dry summers in the south. Thus, the marine climate of the northwest and the Mediterranean climate of the south coast would also be negatives. If the micro-climates of France rate as a risk factor, I would expect that farmers would switch from maize-as-grain to maize-as-silage. especially in the northwest.

Actually, you might approach your study by the inverse method. The evidence of the direction of land-use change might lead you to determine if and where the process is driven by environment and where it is driven by pure risk.

You have chosen a fascinating study. In my opinion, the most interesting aspect is that the process driving land-use change may be partly Brownian (pure risk with no specific cause) and partly systematic (climatic variability). There may be no standard way to approach the question, so you have to invent your own approach,

For climates of France, this map may be a good start and might be a way to develop ad hoc regions. My suggestion would be to form study regions on the basis of climate categories and to determine if land-use changes within these regions is based on pure risk (Brownian i.e. not coherence across departments over space and time) or if there are residual systematic drivers of shifts in land use, i.e. land use shifts from maize-as-grain to some preferred land use.

Koppen climates of France:

Belda et al have updated the Koppen / Trewartha classification. There is a little wet bias in the data they used, but probably not significant for France.

_www.int-res.com/abstracts/cr/v59/n1/p1-13/

To sum up all of this, I suggest from experience of my own hypothesis-driven research, to focus rather on the economic issues. I suggest that you explore the data to determine to what extent various models might "explain" what we see in the real world.

This way all the work that you will do has a better chance of producing something useful that if you decide upon the hypothesis and then try to see if the data fits. If the data does not fit, you end up with a negative, a risky way to spend your time.

Will you please send English translation

Michael Usher is spot on with that recommendation. Simberloff has some good articles about the general theme of eradications of invasive species.  Here are some articles about this theme that I am fond of.

The paradigm of integrating territorial, value chains and livelihoods, as sure pathways to sustainable development based on approach for food, nutritional, health and livelihood security through climate smart territorial concept.

Botanical gardens act as a way of conserving some of the useful and endangered plant species. In so doing the land cover controls soil erosion in addition to conserving some of the highly valued but threatened species

Hy Deepa, you can download the land use change from Global Forest Change (Hansen, et atl)at 2000-2014. there is deribated form Landsat (30 m) and iots veru good for a country or region.

This is the link:

Best regards

Typically in the US, municipal or county governments control land use and zoning regulation and decision making.  Some States have overlay or resource protection controls that are very much land use and planning requirements, however, most states do not go beyond resource protection issues when it comes to land use, planning or zoning.  The state legislature typically adopts a statute that gives these local governments limited authority to adopt ordinances and make decisions on land use and zoning.  The authority typically comes with limits, such as the requirement that the local government adopt planning tools to show how future growth will occur.

Because of this, it is very difficult to put together comprehensive data on the national level.  At most, a state will likely have a list of local government codes put together by its State Planning Department or by the Bar Association group of attorneys that work in this area of the law. I do not think many will have a comprehensive index, but will identify the local code.  The local codes will each have their own index.  This is not comprehensive, and word usage may also vary, so the ability to "search" throughout a state or region is not likely possible.  There are national planning and zoning treatises that teach, generally, how the system works on a national level, but those will not give specifics as to what is done in a particular county or town.  Only the local codes give that kind of detailed information.

Hello dear brother

Gangs that are usually in the realm of 400 to 700 nm because the plant is not appropriate in this dark realm.

The sensor TM: bands 4 and 5 are

Of course, better to eliminate complications such as albedo effects of topography and vegetation to detect ratios of the band.

If we vegetation appears bright for about 700 to 1,300 nm, located in the territory of gangs that gangs in the territory of 400 to 700 or 1300 to 2500 nm are preferred.

The sensor TM: the bands TM4 / TM3 are appropriate. The ratio of TM5 / TM7 are also suitable. But because most of the clays are clear in this territory their use is not recommended.

See the Site: www.fadak.us/A.database/mine/learn/L.004/A.0195.htm

with respect

Hi Manuel

I found this article really useful, specially in thinking about practical examples of some of the "tenets", if you wish, of social-ecological systems. 

Liu et al 2007

Some other more recent work by the same group goes in detail about some of these issues:

Hull et al 2015 on feedbacks: http://www.ecologyandsociety.org/vol20/iss3/art17/

Liu et al 2016 on Telecoupling: http://onlinelibrary.wiley.com/doi/10.1002/16-0188.1/abstract

Cheers

Look up expert publications by UNDP on the topic

Hi Craig, you have to evaluate predicted land use suitability versus empirical data, can take the diffrences about de hectares or kilometers and calculate the accuracy in %.

Regards

I would recommend ht-index, as an alternative metric to fractal dimension, to characterize landscape fragmentation. It help overcome some limitations of fractal dimension, yet is able to capture the underlying complexity of geographic features.

What underlies the notion of ht-index is a fractal way of thinking:

Hi Namita, the LandSHIFT modelling framework was developed at the University of Kassel. One of the authors of the attached paper / links should be able to help you.

Regards, John

Assuming you have acquired your RS scene with enough spectral bands there are several ways but what I usually do: :

1. Geo-rectify image if it is not already done (UTM WGS 84)

2. Run an unsupervised classification (you need to decide how many classes you want)

3. Take output of the unsupervised classification and go out to the field with your GPS unit to define these classes

4. you can merge classes if they are the same

another alternative is to create a training sites where you are 100% about each class

and then run a supervised classification

 I will advise you to same way: Erdas imagine Accuracy assessment tools to evaluate your LU/LC also you can use kappa index but it is not usefull at latest studies. You will use maximum likelihood algorithm and accuracy measures like 0.82, 0.90  ....

Recommended paper: "Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment", by Robert Gilmore Pontius Jr & Marco Millones: 

Cheers

AG

Environmental change impacts more on poor persons and countries. Change in the use of the oceans (including seabed) and the atmosphere, the global commons, may have more impact both for the global and local community. And our knowledge and institutions seem less developed for mitigation of negative impacts.   

Just a little practical case as example on the disparate attention to land versus ocean from an economic and ecological perspective (attached). 

Economic location criteria are then all about transportation and access costs. I cannot at the moment, think of any single article that would cover all these, but search for location theory and transportation cost models. Transportation here would include persons, goods, power, water, garbage, and sewage.

Hi Karel,

I agree that it can be quite painful to find the (geo)data that should be provided by official agencies. I think that you can extract the data you need for EU from Eurostat web pages - there are official geodata sets of NUTS and more particularly also communes, from which more detailed levels can be extracted.

You can also check Diva-GIS and GADM.org for other boundaries but I am not sure whether they are in the desired scale. It also might be possible to extract data from OSM.

Please see the links for details.

Regards

Lukas

In 2013-2014, I conducted a study in partnership with the International Water Management Institute in Vientiane, Lao PDR using biochar-induced treatments on soil quality, crop (water spinach) growth and yield, irrigation groundwater quality for agricultural productivity specifically factoring the effects of soil-water salinity (pH, EC and TDS) and soil sodicity (SAR). The results of our study demonstrate that biochar-induced treatments significantly reduce salinity and sodicity. For this study, biochar were produced from one of the common agricultural biomass (feedstock) in the region - rice husk. Visit my page or find me at www.academia.edu and read some of our reports. I would suggest that using biochar that are sustainably produced (from agricultural waste) would make more sense.

hi

yes why not

Hossam

Hello Mukete,

It is important to keep in mind that the procedures and methods used to detect change and monitor an area of interest can be different for highly vegetated areas (such as your site I believe) than for those used in not so highly vegetated sites (e.g., arid to semi-arid landscapes).  At times change detection may be more successful if the analyses includes looking at not only differences in the spectral characteristics but also the spatial characteristics at local, intermediate, and regional scales (with the same spatial resolution images; for example, using Landsat TM --- or World View or whatever --- look at the spatial information of a 5x5, 31x31, and 101x101 pixel windows --- so not different resolution data sets).

One last thing: for change detection radiometric calibration (including atmospheric corrections) can be very important (can be absolute or relative).  Below are links to two  papers within research gate related to =: 1)  spatial variability differences in the NIR and Visible bands and 2) atmospheric correction and how a simple DOS can be as good as, or better, than most other methods for highly vegetation areas.

Shows that the spatial variability in highly vegetated areas is higher in the NIR band than the Visible bands --- this is important when the area of interest is highly vegetated and the application is for both spatial and change detection analyses:

Image based atmospheric COST correction model and shows that for highly vegetated areas that a simple DOS type correction does as good as, and at times better, than most other techniques for visible bands --- important for change detection using visible bands for highly vegetation areas of interest:

Dear Zahra,

Most of the LULCC models available in literature are case specific. Hence, not of much use in other areas, as no model can be taken as standard model. However, since the spatial extend of your area of interest is small, better to use Land change modeler as given in IDRISI. But before using the module, you must create LULC map for two time period having common LULC classification, using high resolution satellite data of the same month or season.

If you are used to statistical analysis and GIS you can develop your own model for land use dynamics in your study area. 

Interesting project, question and approach to use research gate for this. 

I would argue for research which contributes for assessing the efficiency of land use from the point of view of sustainable development. Many policies (including those that explicity aim for SD goals) have implications for land use: protection of BioDiv, Bio fuels and use of bio based raw materials, renewable energies, organic food, etc. Is all this coherent? How can efficiency of use measured and inform decision making, while considering also non-monetary values (or alternatively improving the data base for monetarization). 

Best Klaus 

I would also like to get a workaround this.I have been trying to install the STDM but I haven't gotten around it. My problem is I already have a working QGIS and Postgresql and I would not like to remove them. How do I get the STDM working in this case? I will appreciate any help.

Hi Philippe,

In France like in most countries national law requires to protect land use for environmental reasons but do not take into account agricultural values.  At the next level, making land use plans is a competence of local governments.

Poland has restrictions related to the agricultural value of soils, aiming to avoid the consumption of high quality soils for urbanization.  A good specialist is Anna Bielska, university of technology Warsaw: a.bielska@gik.pw.edu.pl

Kind regards

Anna Geppert

Hello Brittany,

                                      i am sure you can get some useful information on this site belonging to the departments of Land, Australia on the sale or lease of Crown (State) land : http://www.lands.wa.gov.au/Crown-Land/Sales-Leases-and-Licences/Pages/default.aspx

Regards,

Banson

Land suitability of any area including hilly tracts depends upon the texture and structure of soil, fertility status, topography, water availability and soil and water conservation possibilities through terrace, contouring etc, possibility of human and mechanical power use, suitability of land for crop and livestock enterprises etc.

Thanks Xiaoguang.

I concur with Nathan that Marxan or a similar program could help with this. I would also add that once the data is prepared and an objective function is defined, one could use optimization in any program (such as R). The key would be to get the spatial information together in the required formats, which is what Marxan and other programs provide assistance with. 

Hi Nicolas,

I am currently working on a European project called recharge green, whose objective is try to assess the impact of renewable energies on the ecosystem. For this purpose we estimated several economic benefits of ES and put them into maps, to be integrated in a spatially-explicit decision support system that allows a better power plant siting. We have some papers in review and a technical report will be available online by the end of this week. If you are interested I will send you the report as soon as it is ready, in the meantime I atatch the link of the project website: http://www.recharge-green.eu/

Please go through the attached links they may be useful to you.

Hi,

As Sayed noted, your question is a bit unclear. However, concerning informal settlements in general, there is no universal definition for these locations/areas. What is considered informal in one location may or not include land tenure. Sayed also notes that you should first start with a definition based on location and then move on from there. Its important to note, and I'm not discouraging you in any way, that acquiring information on the locations of informal settlements is often quite challenging for a number of reasons e.g. sensitivity of locating human populations, inefficient systems in governments providing for the disbursement of such information and of course, and most population I think, no information has been collected. These areas can exist both within (e.g. India) and the outskirts (e.g. Iran) of cities. History has a large part to play here because of previous colonialism in many developing countries casted poor communities to specific locations. Of course much have changed since then, but then again, we are often still in a state of stasis well after independence in many parts of the developing world. Each community has its own individuality just in case you venture further into comparison of inter community differences. The United Nations "The Challenge of Slums" report may be worth reading if you work with informal settlements.

Dear Mohammad ,

As natural factors I mention: topography, distance of flooding areas and proximity of green areas.

As urban factors: the presence of infrastructures, services and residential use of high income.

 Best Regards,

Vanessa

SAR= Na/ (Ca+Mg)/2 (where, all ionic concentrations are expressed in milli equivalents per litre.) There were no much variations in SAR concentration between 2005 and 2008. In 2005,in pre­ monsoon period SAR values are >26 in north eastern portion and the lowest ratio locations of <10 were observed in north western, southern parts of the District and it stretches further west to eastern parts of the study area.

Dear Colleague,

You might find it useful to compare your computations with national-level estimates made by FAO for every country in the world, in relation to GHG emissions from drained organic soils. While drainage of organic soils is linked to different types of cultivation, in the areas of your interest the link with palm oil plantation is fairly robust.

You can begin consulting estimates of CO2 and N2O emissions here:

you can also check emissions from degraded peats on the land use part of the database--also in part linked to land use change for palm oil.

If this is of interest, you could contact us for access to the more detailed GIS-based info underlying the national-level numbers.

Best,

Using Decision Support Systems such as MicroLEIS you can evaluate natural resources within two major topics: (i) strategies related to land use planning at a regional level: segregation of agricultural lands, restoration of marginal areas, diversification of crop rotation, and identification of vulnerability areas; and (ii) those related to land management planning at a farm level: organic matter restoration, formulation of tillage practices andworkability timing, optimum machinery use, and input rationalization.

See below publication:

De la Rosa, D., Anaya-Romero, M., Diaz-Pereira, E., Heredia, N., Shahbazi, F.

2009. Soil specific agro-ecological strategies for sustainable land use. A case study by using MicroLEIS DSS in Sevilla Province (Spain). Land Use Policy, 26, 4.

Anaya-Romero, M., Abd-Elmabodab, S., and De la Rosa, D. 2010. Soil erosion and contamination risks for identifying and management of water quality vulnerability zones. A planning approach by using MicroLEIS DSS in Andalucia region. Ambientalia Special Issue “Ten years of the Water Framework Directive” Section V: Water and Spatial Planning.

Anaya-Romero, M., Pino, R., Moreira, J.M., Muñoz-Rojas, M., De la Rosa, D. Analysis of soil capability versus land-use change by using CORINE Land Cover and MicroLEIS in Southern Spain. Int. Agrophysics 25(4), 2011.

There are multiple drought indexes developed in USA to evaluate the drought effects. You can check them in this link: http://www.ncdc.noaa.gov/temp-and-precip/drought/drought-tools.php

Just using seasonal vegetation index (VI) from remote sensing may not be enough for evaluating the drought effect. You may want to use climatology data and long-term remote sensing VIs to find the anomaly of the vegetation. Besides VI, soil moisture, surface temperature observed from satellite can also be used for drought monitoring. But they will require more complex processing, usually with models.

Even without active management, mobile pastoralists in North Cameroon avoid a Tragedy of the Commons, by distributing themselves following so-called Ideal Free Distribution. You may have a look a various papers in my RG profile, of which I just refer to two examples 

Yes, there are volunteers to be advisors if not supervisors for PhD students in developing countries. I agree with this statement and also @ Prof.Olena Dubovyk, @ Pro. John Jennings and @Prof.João Paulo Hespanha answers all are very nice one.

Thanks Prof.Asmat Ali for your valuable question please. 

Polasky et al. 2008. Where to put things? Spatial land management to sustain biodiversity and economic returns. Biological Conservation 141: 1505-1524.

Groot et al. Exploring multi-scale trade-offs between nature conservation, agricultural profits and landscape quality - A methodology to support discussion on land-use perspectives. Agriculture, Ecosystems and Environment 120: 58-69.

 

Do you have any soil maps of your area. That would be very important, and a good place to start. Soil and topography are going to be essential to the flow of water, and the stability and productivity of soil.

 

 

 

 

These link will be useful for you.

 

He Wendy,

We have had work on a similar research questionnaire (Project: PIROL at the University of Applied Sciences Osnabrueck / Germany, 2004 – 2009).

We have an internet-pages, but mostly in Germany (sorry): http://www.pirol.hs-osnabrueck.de/13625.html

I check the public and found a poster, that probably can answer a little part of your questions:

Best regards from Osnabrueck, Stefan

 

Dear Marc Oliver,

Thank you very much for very informative comment and the interesting link. CityGML3.0 seems to be a great project.

I thank everyone for the suggestions.

Here, in Indonesia, we try to valuate the land using non market valuation and try to adjust the land value using total economic valuation method. Here is the example study and still need to be developed:

Land degradation in the Zoige alpine wetlands and its effects to soil organic carbon: http://dx.doi.org/10.1016/j.ecoleng.2012.12.020

Have you tried to convert your data series into signals so that you could work in the frequency domain for instance, using Fourier or wavelets. It won't tell you what triggers what, but it will allow you to have an idea if your dataset are related and at which scale (the idea with the wavelet analysis, allowing you to remove let say a general trend or noise...etc in your signal).

Dear Chitrini,

I think that you should carefully differentiate two types of scenarios:

1. Exploratory scenarios (what could happen?)

2. Normative scenarios (what is your desired future?)

As far as I understand, you have developed 4 exploratory scenarios and you have one plan, which could be seen as a normative scenarios. Comparing both products can obviously be useful, but please keep in mind that the master plan has been developed by someone with some kind of exploratory scenario (probably a Business As Usual) in mind. As such, you are comparing apples and pears. An explanation of the method that I have used can be found in:

Kok, K., van Vliet, Bärlund, I., M., Dubel, A., Sendzimir, J. 2011. Combining participative backcasting and explorative scenario development: Experiences from the SCENES project. Technological Forecasting and Social Change 78(5): 835-851.

I hope this is helpful!

Kasper

Thanks Max. I used both Idrisi Land change modeler and DynaCLUE for my modeling. However, both do not include the demand calculation with projected population. However, DynaCLUE suggests using system dynamic model for its non spatial part and calculate land demand based on population growth. I am trying a system dynamic model using STELLA now. Thanks for your reply and the links. As I observed, most of these land use models provide spatial land allocations for the given land demands. I am still trying to find other methods to calculate land demand based on spatial as well as non spatial inputs of population growth and projected population.

By the way, Caroline: "Ke" is a half of my given name "ke Chung"; how would you like to be called "Caro." Aside a classification of species' status, it is necessary to monitor the status of "important" species or species of concern that includes pest species. In most cases I got involved with major problem is to "monitor" the species without knowing what you have in a community or ecosystem of your interest. Thus, it is necessary to start with having the baseline information of the system which contains your target species along with its ecological cohorts. Such assessment should open up your curiosity for the state of species in relation to the structure of a monitoring unit. Such data should help you understand the status of its biodiversity ass well which makes community ecology very fascinating. Ke Chung Kim

Avit Bhoumick has shown you a easier way but it is academic, useful in completing an academic project. Kasper has shown you a right way if you intend to predict really. I am showing you the way assuming that you are on task of planning and want to reach implementable conclusion.

Firstly future is unpredictable. In history we do not find any example where the prediction was done in land use simply because transitions depend upon numerous variables of development in future. e.g. Govt. policies to people's attitude to technological development. YET PREDICTION IS MANDATORY FOR PLANNING THE FUTURE.

Hence the best way is to try to study THE PRESENT TREND INSTEAD OF TRYING TO SURVEY THE PAST TREND, that does not really help. Primarily because the trends do also change, forward backward, left right. One should take up a huge survey and opinion collection of a huge no. of people from various walks of life. PARTICULARLY NEW GENERATION. Their aspirations, expectations, direction of thoughts, govt. plans, Success and failure of plans, Corporate sector, International scenario, Agriculture, expansion of urban areas, whether the migration may reverse (Strong reversal trends are seen in some regions where rural development in form of rural industries is taking place fast) and what not. IT IS A HUGE TASK. But in my opinion there is no other realist way. To collect information, to find a futuristic trend out of it, predict periods, directions, scale etc. is further difficult and should be done with collective analysis. (Not by a single brain) You will have to live long to see the extent of success of your planning done on the basis of these prediction. EVEN YOU SUCCEED 10% YOU SHOULD PAT YOURSELF.