International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com SPATIAL TEMPORAL CLIMATIC CHANGE VARIABILITY OF CROPPING SYSTEMS IN WESTERN Dr. Avadhesh Koshal, Project Directorate for Farming Systems Research, [email protected]

seasons, there is a short season during the summer months Abstract known as the zaid season [4]. The important rabi (winter /spring

The Western U.P. is the part of agricultural tracts in Uttar crop) crops are Wheat/gehu (Triticum aestivum), Barley /jau Pradesh. The Coordinates of are as (Hordeum vulgare), peas/matar (Pisum sativum), chickpea/gram following: Latitudinal extent - 29° 58′ 12″ N to 26° 28′ 12″ N (Cicer arietinum) and Rapeseed/Mustard (Brassica juncea & Longitudinal extent - 77° 35′ 0″ E to 80° 6′ 0″ E. The statistical rapa). Availability of precipitation during winter months due to analysis of crops and cropping systems which are based on Net the western temperate cyclones helps in the success of these Sown Area (NSA), area and yield data fifteen years observed crops. Western Uttar Pradesh is important for the production of maximum Net Sown Area (NSA) percentage 57.09%, 22.38% wheat and other rabi crops. The maximum kharif season and 18.21 of wheat, sugarcane and rice crop respectively. The 2 (monsoon crop) cropped are rice/paddy (Oryza sativa), major crops yield trend (linear) is observed in graph R value maize/corn (Zea mays), sorghum/jowar(Sorghum bicolor) 0.33. The major cropping systems are observed Sugarcane- pearlmillet/bajra (Pennisetum glaucum) , pigeonpea/arhar wheat, Rice-wheat, Maize wheat, Pearl millet-wheat and (Cajanus cajan), green gram/ moong bean (Vigna radiate), black Sorghum-wheat. The fifteen year cropping systems analysis 2 gram/urad (Vigna mungo), potato/Aloo ( Solanum tuberosum ) observed R = 0.27. The analysis of monthly data of cotton/rui (Gossypium hirsutum), groundnut/ moongphali quinquennium period (1996-05-10) observed rising or decline (Arachis hypogaea), and soyabean/soy (Glycine max) . The temperature due climatic change. The scenario of changing 2 major crops produced during Zaid are watermelon/tarbooz pattern in overall analysis found 07-2.2°C. The R values of (Citrullus lanatus), muskmelon/Kharbooz (Cucumis melo), maximum and minimum temperature are observed 0.15 and 0.12. cucumber/kheera (Cucumis sativus), vegetables and fodder crops The 15 years (1996-2010) analysis of data coefficient of . Sugarcane (Saccharum L.), takes almost a year to grow [5]. variation observed value 0.11. The major cropping systems viz. rice-wheat, maize-wheat, In ERDAS IMAGINE, create a pseudo colour table to set the sugarcane/ratoon-wheat, maize-mustard and Pearlmillet – value 0 is red/orange color, green/yellow ranges are between are groundnut are grown in Western U.P. The cropping systems are 255. The time series remote sensing SPOT VGT data is useful to changing due to changing of climatic pattern. In terms, understand changing of major cropping systems. The major traditional crop grown areas changing other crop grown areas climatic parameters viz., minimum/maximum temperature and due to rising temperature, heavy rain, drought or pest impact. It average /normal rainfall are important for crops and cropping may be other factors viz. cash crop gives more profit than systems. The changing of climatic parameters is adverse affected traditional crop like herbal crop (medicinal plants) and on the grown areas. horticultural crops (seasonal flower). Cropping system level study is not only useful to understand the overall sustainability of Introduction agricultural system, but also it helps in generating many important parameters which are useful in climate change impact Agriculture and associated industries are the primary source of assessment [6]. Climate change will increase both abiotic food and the major employment sector. The agriculture sector stresses, such as drought, and biotic stresses, such as pest and contributed 17.2 per cent; industry contributed 18.5 per cent crop disease pressures, on agricultural systems Climate is one of while the service sector had a contribution of 64.5 per cent of the the most important input factors in the agriculture productivity in GDP according to 2008-09 estimates [1]. Uttar Pradesh, has all over the world. Pattern of temperature and precipitation are geographical area of 2,40,928 Km2 which constitutes 7.3% of the changing due to global warming, resulting in having impact on total area of country [2]. Western UP is one of the prosperous crop productivity [7]. The climate is the overall pattern of the agricultural tracts of the country [3] In Uttar Pradesh (plains) weather in a region over a long period of time. Climate change is roughly 18% of gross agricultural output is sugarcane while its one of the most important global environmental challenges of the share in area cultivated is only around 6 percent. Western U.P. present century. The most visible impacts of climate change are crop calendar has three cropping seasons- rabi, kharif and zaid. the increased global mean surface temperature; increased The kharif cropping season is from July to October during the frequency and severity of drought, variations in precipitation, and south-west monsoon and the rabi cropping season is from increased heavy precipitation events. All these manifestations October to March (winter). In between the rabi and the kharif have a significant impact on world agriculture. As per the findings of the study conducted by the Indian Agricultural Research Institute (IARI) New , with every 1ºC. increase in ISSN No: 2319-3484 Volume 2, Issue 3, May 2013 36 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com temperature throughout the growing period of the crop, the total geographical areas of UP 240,928sqkm and western UP has overall wheat production may be lost by 4 to 5 million tones [8]. 72,018 sq.km percentage of total area is 29.6 % rest area 70.4% According to new reports by the Consultative Group on UP. Coordinates of Western Uttar Pradesh are as following: International Agriculture Research. Important crops like maize Latitudinal extent - 29° 58′ 12″ N to 26° 28′ 12″ N Longitudinal and wheat produce less grain at temperatures above 30 degree extent - 77° 35′ 0″ E to 80° 6′ 0″ E. It lies between the two Celsius [9]. important streams – the Ganga and the . Western Uttar The statistical information on crop area, production and Pradesh shares borders with the states of , , productivity form the backbone of agricultural statistical system. Delhi, and , as well as a brief Time series of optical satellite images acquired at high spatial international border with Nepal in . It is one of resolution is a potentially useful source of information for the fertile regions of the state of Uttar Pradesh [17]. The climate monitoring agricultural practices [10]. Satellite remote sensing of the region is tropical monsoon, Rainfall ranges from 600 to and GIS technology are now widely used for environmental 1,000 mm (24–39 in) in the western Uttar Pradesh. About 90 monitoring and mapping the distributions of land surface percent of the rainfall occurs during the southwest Monsoon, biophysical parameters that have an important effect on climate lasting from about June to September. Study area is represented [11]. A wide variety of satellite remote sensing data from three major seasons, viz., hot weather (March-June), rainy season LANDSAT – TM, SPOT, IRS 1C & 1D, CARTOSAT & (July-September) and cold weather (October-February) [18]. The RISAT-1 are now available to earth resource scientists for south-west or the summer monsoon is the main source of rainfall. generating information on natural resources. Remote sensing The soil tends to be lighter-textured loam, with some occurrences provides tools for advanced cropping system [12]. Multi of sandy soil. Western Uttar Pradesh consists of twenty three temporal remote sensing data are widely acknowledged as having districts [19] & [20], which are grouped into 6 commissionaires significant advantages over single date imagery [13] for studying as following: Division, Division, dynamic phenomena. GIS (Geographical Information System) Division, Division, Division & Division refers to computer software that provides for data storage, [21]. retrieval and transformation of spatial data. A GIS consists of two major elements namely hardware (processing unit, plotter/printer and graphic display system) and software (ARC GIS, ILWIS, IDRISI, MAPINFO & GRASS etc.). Material Simple remote sensing indicators derived from SPOT VEGETATION instrument can be successfully used as a source The present study is based on secondary sources of time series of crop yield predictors for the Mediterranean and Central Asian data (Area and yield) data of major crops of obtained 15 years countries. The VGT imagery has a spatial resolution of 1 km. It 1996-99 to 2010-11. To achieve the stipulated objectives, the is therefore more suitable for national and regional monitoring of present study had been carried out on the basis of time-series major seasonal variations in vegetation patterns. Time series of data pertaining to continuous quinnquious three time periods viz. optical satellite images acquired at high spatial resolution is a 1996-2000, 2001-05 and 2006-10.have been collected from potentially useful source of information for monitoring the published records, cropping systems atlas [22] bulletin of the agricultural practices [14]. In this paper the SPOT data Directorate of Agricultural Statistics and the Institute of State Vegetation index is used to monitor the vegetation cover change Planning, U.P., ICAR , DRR, INARIS, Agricoop and other in Western U.P. The aim of this study is to establish the spatial national level institute [23]. The climatic data (total, actual and and temporal changes in vegetation cover and their relation to the normal rainfall, minimum and maximum temperature etc.) of climatic parameters. western U.P. of continuous 15 years 1996-99 to 2010-11 data were obtained from IMD, Pune [24], [25], [26] & [27]. The trend pattern of normal rainfall data in the study area Objective boundary feature file was used for GIS layer and integrated MS excel generated *.dbf to .shp file for geostatistical analysis. The The time series remote sensing SPOT VGT data is most important and common tool of geostatistics is local useful to understand changing of major cropping systems in estimation and prediction in ARC GIS. The time series remote Western U.P. due to major climatic parameters (temperature and sensing data used for this study included ten-day composite rainfall). NDVI products of SPOT- VEGETATION (VGT) sensor for the period [28]. The data S10 was downloaded from the VGT [29] Study area free data product Internet site (http://free.vgt.vito.be). SPOT vegetation (VGT) has been found very useful to study the

dynamics of agricultural system at regional level. The digital The proposed study area (Fig.1) belongs to Upper Gangetic map of with different states was used and states Plains Region (Agro- climatic Region) under Indo Gangetic information was taken from planning commission report. The Plains Region (IGP). Western U.P is specific parts of Ganga time series images (map) were prepared in ERDAS IMAGINE Plain Region [15]. It covers highly fertile plain, upper, middle and part of lower Ganga Yamuna [16]. The ISSN No: 2319-3484 Volume 2, Issue 3, May 2013 37 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com

8.2 and geo referencing/rectified and masking the area (India) in The single band was stacked to create temporal series data (year: ARC GIS 10 software. 2000, 2005 & 2010) of initial March (rabi) and initial August (kharif) month. The images were convert in digital numbers (DN Values) based in to series of classes, so there corresponding all Method the dates were generated from DN values. The numbers of gray levels classes were identified based on colour range. Statistical Analysis Result and Discussion It is inevitable to bring crops and climatic data into one common format preferably in excel. Handling and Major cropping systems analysis analyzing the data in this format is easy and conversion to other formats. Year-to-year fluctuations in summer monsoon rainfall over India have a strong impact on the variability of aggregate kharif food- Trend analysis grain production [36] & [37].The summer, or ‗kharif‘, growing season (June–September) coincides with the southwest monsoon. Trend analysis of a time series consists of the magnitude Depending on crop duration, kharif crops can be harvested of trend and its statistical significance. In general, the magnitude during the autumn (October–November) or winter (December– of trend in a time series is determined either using regression February) months. The shifts in cropping pattern in the western analysis (parametric test). Regression analysis is used to relate Uttar Pradesh, which consists of 23 districts were studied on the crop yield data to weather data considering the same area [30]. basis of secondary data 1996 to2010. To reduce the irregular and Regression analysis approach is useful in providing quite cyclical fluctuations in these years, 15 years average for 1996- effective estimates of crop yield when crop yield is affected by 2010 and for the rest of the periods, 5 years average were worked weather factors such as rainfall or temperature [31]. Thus out (1996-2000, 2001-05 &2006-10). regression analysis is used in the present study to estimate impact After analysis Western U.P. major crops data of fifteen years of temperature and rainfall on rice productivity. Analysis of observed maximum Net Sown Area (NSA) percentage 57.09%, climate and crop production statistics in the different regions of 22.38% and 18.21 of wheat, sugarcane and rice crop the state can provide a handy tool in understanding the soundness respectively. The lowest percentage of net sown area of crops is of the existing cropping systems. observed 0.32 and 0.44 in sorghum and groundnut crops Statistical analysis of spatial and temporal crop data is an respectively. effective tool in understanding the logic behind existing cropping The analysis of quinquennium (5years: 1996-05-10) data of pattern. Analysis of climate and crop production statistics in the major crops observed rice and potato crop net sown areas 14.88 different regions of the state can provide a handy tool in and 4.45 in year 2006-10 but decline in net sown area understanding the soundness of the existing cropping systems percentage 0.99 and 0.18 observed in Barley (Jau) and Sorghum [32]. The best index of the suitability of an area for a particular (Jowar). After analysis Western U.P. major cropping systems are crop is its relative yield as well as its stability from year to year observed maximum Net Sown Area (NSA) percentage 57.1%, as judged from co-efficient of variability (CV) regardless the 39.7% and 37.6 of fallow-wheat, sugarcane-wheat and rice-wheat area under the crop [33]. The above data sets are analyzed by cropping systems respectively. The lowest percentage of net applying different statistics techniques like linear regression, co- sown area of there cropping systems is observed 5.2 and 1.6 in efficient of co-relation, Pearson –co-relation. Pearson co-relation Maize-mustard and Fallow-barley cropping systems respectively. is a measure of association between two datasets having value of The analysis of quinquennium data of major cropping systems ―+1‖ or ―-1‖. In this study an attempt has been made to discuss observed Rice-wheat and Pearlmillet-potato systems percentage the temperature trend for both data sets on regional level net sown areas 36.8 and 35.5 observed in year 2006-10 but (provisional level) [34]. decline in net sown area percentage 1.0 observed in Fallow- The study established the methodology for spatial barley shown (Table-1). The percentage of dominate sugarcane- analyst of crop mapping [35]. The utilized data for research wheat cropping systems has not much changes during year 2006- included temporal remotely sensed data (SPOT VGT), climatic 10. The analysis of secondary data of fifteen years (1996-10) data (rainfall, minimum and maximum temperatures data), and quinquennium (5years: 1996-05-10) data analysis identified agricultural data (crop/cropping systems area and yield) and major crops and cropping systems. The major crops viz. digital maps (state & districts map) of western U.P. The satellite sugarcane, wheat, potato, maize and mustard are dominate in this images were download from website and pre-processed. This area. Those crops are grown in Rabi, Kharif and Zayad season. included the importing of .hdf to .img format images into a The cropping systems generated with help of cropping systems standard format of the ERDAS IMAGINE 8.2 Then the dataset atlas to develop current cropping systems. were geometrically corrected into the WGS84 Geographic lat/ The quinquennium data of major crops in Western U.P. based on long projection system. The study area subset with a vector Average Yield percentage observed Sugarcane, wheat and barely polygon file (.shp file) representing the area boundary (AOI). are major crops having 25to 60 percentage of yield. The analysis ISSN No: 2319-3484 Volume 2, Issue 3, May 2013 38 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com of quinquennium graph is observed R2 values 0.32, 0.33 & 0.32. Rainfall is important for food production plan, water resource The series of trend analysis showed little changes in major crops management and all activity plans in the nature. The occurrence pattern in linear trend graph. The yield (t/ha) linear trends of of prolonged dry period or heavy rain at the critical stages of the quinquennium graph have R2 values 0.28, 0.27 & 0.25 crop growth and development may lead to significant reduce respectively. The sugarcane-wheat system is dominant cropping crop yield. system and Pearlmillet-wheat system observed less dominant The 15 years average rainfall was 684.8 mm observed. The system. The fifteen years (1996-10) data analysis of major crops quinquennium (5years: 1996-05-10) overall data of Western observed maximum yield percentage of sugarcane, wheat, barely U.P. observed lowest actual rainfall 596.3 mm in year 2006-10 and potato have 57.4%, 31.69%, 26.62% and 22.74% (Table-5). The trend analysis of quinquennium trend data respectively (Table-2). The major crops yield trend (linear) is analysis observed R2=0.79 (Fig.5).The rainfall data variability observed in graph R2 value 0.33 (Fig.2).The quinquennium observed in quinquennium year 2006-10 data analysis due to average yield percentage data of major cropping systems of changing of rainfall trend in Western U.P . Western U.P. observed maximum change in Maize-wheat, Pearl millet-wheat, Maize-mustard and Pearl millet-groundnut Spatial analysis of rainfall data systems (Fig.3). The importance of predicted rainfall towards planning and The analysis of the fifteen years (1996-10); (Table-3) major ensuring food security for the country is vital; rainfall is cropping systems are observed Sugarcane-wheat, Rice-wheat, extremely important to agriculture. The mean rainfall 1996-2010 Maize wheat, Pearl millet-wheat and Sorghum-wheat. The fifteen of geostatistical pattern analysis; prediction map was created. year cropping systems analysis observed R2= 0.27. The cropping The trend pattern of normal rainfall data of different districts of systems have not much change due to stability of cropping Western U.P. gives pre information of distribution of crops and systems in districts of Western U.P. These systems give more there cropping systems. In the created map were observed most yield than other systems viz. Maize-mustard, Pearlmillet- of the districts have good normal rainfall (>800mm). The main groundnut and Pearlmillet-potato. The sugarcane-wheat districts are belonging to Sharanpur, , Bijnore, system is dominant cropping system has more yield Rampur and Shajhanpur (red colour). The lowest rainfall percentage. Due to this system called sugarcane bowl state. (<600mm) observed in (orange colour) Agra and districts (Fig.6). Climatic parameters analysis Impact of climatic parameters (rainfall &temperature) on cropping systems Minimum &maximum temperature data analysis Rainfall variation, water loving crops are grown more and crop The time series (monthly) data of western U.P. of minimum and yield increase, controlling for other factors at constant, whereas a maximum temperatures covering the period from 1996 to 2010; decline in rainfall is associated with the fall yields. The findings the scenario of changing pattern in overall analysis found 07-2.2 of the study indicate that temperature and precipitation variables °C . The R2 values of maximum and minimum temperature are have significant effects on the crops yield. Average maximum observed 0.15 and 0.12 (Fig.4). The analysis of monthly data of temperature during September- October and July-August and quinquennium period (1996-05-10) observed rising or decline average minimum temperature during July-August are temperature due climatic change. The vulnerable change statistically significant. Rainfall plays a critical role in year-to- observed rising of temperature in current quinquennium period year variability of production for these crops, with a change in (2006-10). growing season precipitation by one standard deviation The analysis of average of quinquennium (5years: 1996-05-10) associated with as much as a 10% change in production. data of Western U.P. observed monthly maximum and minimum Temperature also plays a significant role in driving year-to-year temperature pattern observed (Table-4). In year 2006-10 the production changes, but was slightly less important than rainfall maximum temperature variation are observed due to climatic by this measure in the majority of cases. This result agrees with change. The change percentage not more but little change the intuition that rainfall is very important to agriculture. affected the rabi and kharif season crops. Due to rising or lowering temperature more affected on the seed germination and yield. The minimum and maximum temperature directly affected Spatial data analysis cereal crops in terms of germination and yield (milking stage). Density slicing based classification of SPOT VGT Image The analysis of quinquennium graph observed R2 value of analysis minimum temperature observed 0.13, 0.10 & 0.10 whereas A number of methods have been developed to determine the start maximum temperature R2 value observed 0.17,0.30 & 0.11 and end of growing seasons using time series of vegetation respectively. indices. The SPOT VGT images were downloading in grayscale image /single band converted into colour image due to in colour image easily identify the different crop areas. The pseudocolour Rainfall data analysis tables in gray scale values are mapped to particular colour.

ISSN No: 2319-3484 Volume 2, Issue 3, May 2013 39 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com

The classes are divided in two broad classes to identify the crop sugarcane, wheat, potato, maize and mustard are dominate in this growth pattern from the whole areas. The classification is based area. Those crops are grown in Rabi, Kharif and Zayad season. on computer specified density classes based on digital numbers The quinquennium average yield percentage data of major (DN values) viz. Agricultural areas and Non-agricultural areas. cropping systems of Western U.P. observed maximum change in Slicing based analysis give the very clear view to dividing the Maize-wheat, Pearl millet-wheat, Maize-mustard and Pearl image with arrange of values in to unique classes. In three millet-groundnut systems. The analysis of the fifteen years images of kharif season (August, 2000-05-10) and rabi season (1996-10); major cropping systems are observed Sugarcane- (March, 2000-05-10) have different crops areas; whereas in this wheat, Rice-wheat, Maize wheat, Pearl millet-wheat and month vegetation growth in better stage to other month (Fig.7 & Sorghum-wheat. The fifteen year cropping systems analysis Fig.8). In these month crops health are in good condition and observed R2= 0.27. The cropping systems have not much change images give clear tonal value (DNvalue). In ERDAS IMAGINE, due to stability of cropping systems in districts of Western U.P. create a pseudo colour table to set the value 0 is red/orange color, These systems give more yield than other systems viz. Maize- green/yellow ranges are between are 255 (Table-6). The colour mustard, Pearl millet-groundnut and Pearl millet-potato. The ranges are directly based on DN value. This method is good for sugarcane-wheat system is dominant cropping system has more identification of cropping pattern of grown agricultural crops yield percentage. area in overall area.. Agra, Mathura, , Aligarh, Firozabad The time series monthly data of western U.P. of minimum and and districts are lesser agriculture area of other district areas maximum temperatures covering the period from 1996 to 2010 in of Western U.P. The agriculture districts are Muzaffarnagar, changing pattern of overall analysis found 07-2.2°C. The R2 Shajhanpur, Meerut and Bagpat have dominant rabi and khrif value of maximum and minimum temperature is observed 0.15 crops. and 0.12. The analysis of monthly data of quinquennium period observed rising or decline temperature due climatic change. The Quinquennium Image Mapping of Rabi &Kharif season vulnerable change observed rising of temperature in current The pixel locations of these sub clusters for each cluster are quinquennium period (2006-10). The change percentage not marked with different colors to map the variability in the more but little change affected the rabi and kharif season crops. cropping practices to produce the cropping practice variability Due to rising or lowering temperature more affected on the seed map. The identified seasons were then classified as kharif and germination and yield. The minimum and maximum temperature rabi and as Annual crops manually. Pixel locations for the directly affected cereal crops. various combinations of kharif and rabi were mapped spatially to The importance of predicted rainfall towards planning and produce the crops area map. The SPOT VGT data of rabi and ensuring food security for the country is vital; rainfall is kharif season found rabi season has more cropped area than extremely important to agriculture. The 15 years (1996-2010) kharif cropped area. The rabi and kharif season crops are district wise rainfall data of western U.P., observed ranges of distributed in the Western U.P. area mainly on rainfall, soil and actual rainfall in > 800 mm in Districts viz. Bareilly, availability of resources (tube well, seed, fertilizers or J.B.P.Nagar , and ranges of lowest actual rainfall agricultural market). observed (<600mm) in seven districts Hathras, Agra, Etah, Mathura, Aligarh, Gautam Buddha Nagar and . Conclusion Rainfall is important for food production plan, water resource management and all activity plans in the nature. The occurrence The analysis of fifteen years (1996-2010) and quinquennium of prolonged dry period or heavy rain at the critical stages of the (1996-2000, 2001-05 &2006-10) data (net sown area, areas and crop growth and development may lead to significant reduce yield) for statistical analysis of crops and their cropping systems crop yield. The trend pattern of normal rainfall data of different observed maximum Net Sown Area (NSA) percentage of wheat, districts of Western U.P. gives pre information of distribution of sugarcane and rice crop. The lowest percentage of net sown area crops and there cropping systems. The findings of the study of crops is observed sorghum and groundnut crops. The major indicate that temperature and precipitation variables have cropping systems are observed maximum Net Sown Area (NSA) significant effects on the crops yield. percentage fallow-wheat, sugarcane-wheat and rice-wheat The SPOT VGT images are classified in two broad classes to cropping systems respectively. The lowest percentages of net identify the crop growth pattern from the whole areas. The sown area of there cropping systems are Maize-mustard and classification is based on computer specified density classes Fallow-barley. based on digital numbers (DN values) viz. Agricultural areas and The analysis of Quinquennium data of major crops observed Non-agricultural areas. In three images of Kharif season (August, rice and potato crop but decline in net sown area observed in 2000-05-10) and rabi season (March, 2000-05-10) have different Barley (Jau) and Sorghum (Jowar) .The analysis of crops areas; whereas in this month vegetation growth in better Quinquennium data of major cropping systems observed Rice- stage to other month. In these month crops health are in good wheat and Pearlmillet-potato systems percentage net sown areas. condition and images give clear tonal value (DNvalue). This The percentage of dominate sugarcane-wheat cropping systems method is good for identification of cropping pattern of grown has not much changes during year 2006-10. The major crops viz. agricultural crops area in overall area.. Agra, Mathura, Hathras, ISSN No: 2319-3484 Volume 2, Issue 3, May 2013 40 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com

Aligarh, Firozabad and Etah districts are lesser agriculture area of other district areas of Western U.P. The agriculture districts are Muzaffarnagar, Shajhanpur, Meerut and Bagpat have dominant rabi and kharif crops. The SPOT VGT data of Rabi and Kharif season found rabi season has more cropped area than Kharif cropped area. The rabi and kharif season crops are distributed in the Western U.P. area mainly on rainfall, soil and availability of resources (tube well, seed, fertilizers or agricultural market). The climatic parameters and remote sensing (SPOT VGT) data have key role to analysis of temporal climatic change variability of cropping systems in Western Uttar Pradesh.

Figure 3. Yield (t/ha) of Quinquennium (5years: 1996-05-10) data of major crops & cropping systems of Western U.P.

Figure 1. Study area

Figure 4. Average Minimum &maximum Temperature (°C) , (Year -1996-10) of Western U.P.

Figure 2. Fifteen years (1996-10) yield (t/ha) of major crops and cropping systems of Western U.P.

ISSN No: 2319-3484 Volume 2, Issue 3, May 2013 41 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com

Quinquennium over 15 Years Rainfall (mm) of Western U.P.

800

y = -68.571x + 821.99 R2 = 0.7972

700

Rainfall (mm)

Linear (Rainfall (mm)) Rainfall (mm) Rainfall

600

500 1996-2000 2001-05 2006-2010 Quinquennium (5Years) Figure 5. 15 Years & Quinquennium (5years: 1996-05-10) Rainfall of Western U.P.

Figure 6. Normal rainfall (mm) over 15 years in Figure 8. Kharif season map of Western U.P. derived from Western U.P. Multidate SPOT VGT NDVI data (August: 2000-05-2010)

Table 1. The fifteen years (1996-10) and Quinquennium (5years: 1996-05-10) data of major Cropping System of Western U.P. based on Net Sown Area (NSA)

Net Sown Area (NSA)Percentage of Cropping System Cropping System 1996-10 1996-00 2001-05 2006-10 Fallow-wheat 57.1 55.5 57 58.8 Sugarcane- wheat 39.7 38.7 39.7 40.8 Rice-wheat 37.6 32.3 35.3 36.8 Pearlmillet- wheat 34.4 33.5 34.3 35.5 Maize-wheat 31.1 30.7 30.9 31.5 Sorghum- wheat 28.7 28 28.6 29.5 Pearlmillet- potato 7.6 7.1 7.4 8.3 Figure 7. Rabi season map of Western U.P. derived from Pearlmillet- Multidate SPOT VGT NDVI data (March: 2000-05-2010) groundnut 6.0 6.0 6.0 6.3 Maize- mustard 5.2 6 4.9 4.7 Fallow-barley 1.6 2.1 1.4 1.0

ISSN No: 2319-3484 Volume 2, Issue 3, May 2013 42 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com

Table 2. The fifteen years (1996-10) & Quinquennium Table 4. Monthly average of minimum & maximum (5years: 1996-05-10) data of major crops of Western U.P. Temperature (°C), Quinquennium of Western U.P. based on Average Yield Percentage (AYP) Average Maximum Average Minimum Temperature Temperature Major Crop of Average Yield percentage (Quinquennium years) (Quinquennium years) Crop 1996- 2001- 2006- 1996- 2001- 2006- 1996-10 1996-00 2001-05 2006-10 Month 2000 2005 2010 2000 2005 2010 Sugarcane 57.40 59.00 57.42 56.88 January 21.2 20.5 22.6 7.0 7.8 8.3 Wheat 31.69 31.55 31.16 32.35 February 25.0 23.9 26.4 10.2 10.9 12.8 Barley 26.62 26.44 26.02 27.45 March 30.8 30.5 32.7 15.0 16.2 17.8 April 37.8 36.0 38.2 21.7 21.1 22.5 Potato 22.74 21.92 23.73 22.53 May 40.6 38.7 39.9 25.5 24.6 27.4 Rice 22.18 22.40 22.35 22.45 June 38.7 36.6 37.9 26.8 27.4 27.9 Maize 16.06 15.90 15.00 17.32 July 35.0 33.5 33.3 26.7 26.2 26.9 Bajra 13.78 13.09 13.37 15.22 August 33.1 32.2 31.7 25.7 24.6 25.5 September 34.0 31.6 31.4 24.3 23.0 23.7 Mustard 10.73 9.48 10.81 11.86 October 33.2 31.2 31.1 19.0 17.7 19.3 Sorghum 8.58 7.51 9.71 8.87 November 29.4 27.9 27.8 13.3 11.8 13.8 Groundnut 8.35 8.62 8.18 8.10 December 23.3 23.1 23.8 8.2 8.6 9.5

Table 5. Average (15years & Quinquennium) rainfall (mm) Table 3. The fifteen years (1996-10) &Quinquennium (5years:of Western Uttar Pradesh 1996-05-10) data of major cropping systems of Western U.P. Average based on Average Yield percentage (AYP) rainfall Quinquennium Quinquennium Year (mm) Year Rainfall (mm) 1996 951.4 Cropping Cropping system Average Yield percentage systems 1997 655.6 1996-10 1996-00 2001-05 2006-10 1998 785.3 1996-2000 733.5 Sugarcane- wheat 89.09 90.55 88.58 89.23 1999 653.1 Rice-wheat 53.87 53.95 53.51 54.80 2000 621.8 Maize-wheat 47.75 47.45 46.16 49.67 2001 593 Pearlmillet- 2002 507.4 wheat 45.47 44.64 44.53 47.57 2003 931.9 2001-05 724.8 Sorghum- wheat 40.27 39.06 40.87 41.22 2004 815.1 Pearlmillet- 2005 776.5 potato 36.51 35.01 37.10 37.75 2006 417.6 Fallow- wheat 31.69 31.55 31.16 32.35 2007 457.7 Maize- 2008 737 2006-2010 596.3 mustard 26.79 25.38 25.80 29.18 2009 511.1 Fallow- barley 26.62 26.44 26.02 27.45 2010 858.1 Pearlmillet- Mean 684.8 groundnut 22.13 21.71 21.54 23.32 76.8 SD CV 0.11

ISSN No: 2319-3484 Volume 2, Issue 3, May 2013 43 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com

Table 6. Overall tonal classifications of SPOT VGT images ndia_paper.pdf (Rabi & Kharif) of Western U.P. [11] A. Henderson-Sellers, ―Land-use change and climate. Land Degradation Rehabilitation‖, 1994, Vol. 5, pp. Classes Ranges (DN values) Tone 107-126. http://ccafs.cgair.org/sites/default/files/assets/docs/medi Non-Agriculture 0 Red/orange a_adivsory_final-11-15.pdf & Area http://ccafs.cgiar.org/node/853. Agriculture 1-255 Yellow/green [12] S. Panigrahy , S.S. Ray, P.K. Sharma, A.K. Sood and Area I.B. Patil, ‗Cropping system analysis using remote sensing and GIS—Bathinda District, Punjab‖, Scientific References Note RSAM/SAC/CS/SN/01/2002. Space Applications Centre Ahmedabad. [1] A.Das, D. R. Banga, and D. Kumar, ―Global Economic [13] J. R. G. Townshend, T.E. Goff, and C.J. Tucker, Crisis: Impact and Restructuring of the Services Sector ―Multispectral dimensionality of images of normalized in India‖, ADBI, Working Paper 311, 2011. difference vegetation index at continental scales‖. IEEE http://www.adbi.org/workingpaper/2011/09/30/472 Transactions on Geoscience and Remote Sensing, 1985, 7. Vol. 6, pp. 888–895. [2] India Forest Report, ―Forest Survey of India, Ministry [14] P.J. Webster, V.O. Magana, TN. Palmer, J. Shukla, R.A. of Environment & Forests, Govt. of India‖, 2011, pp. Tomas, M. Yanai, and T. Yasunari, ―Monsoons: 230-235. processes, predictability, and the prospects for http://www.fsi.org.in/cover_2011/uttarapradesh.pdf prediction‖, Journal of Geophysical Research. 1998, [3] A.K. Singh, ―Regional disparities and cropping Vol. 103, pp. 14451–14510. pattern: Case study of Uttar Pradesh‖ Economic and [15] Planning commission Report, ―Agro-climatic Zones of Political Weekly, Vol.4, No.36, pp.1449-1451,1969. India‖, National Bureau of Soil Survey and Land Use [4] Arthapedia, Planning (NBSS&LUP). Nagpur,2005. http://www.arthapedia.in/index.php?title=Cropping_sea [16] R.P. Singh and Z. Islam, ―Land use planning in Western sons_of_India_Kharif_%26_Rabi Uttar Pradesh issues & challenges‖, Recent Research in [5] Agriculture, Science & Technology. 2010, Vol. 2, No. 9, pp.11-17. http://www.excellup.com/notes/10_socsc_Agricultu [17] H.Rehman, A.Wahab and Asif, ―Agricultural re.pdf Productivity and Productivity Regions in Ganga- [6] C.P. Mohan, ―Cropping Patterns and Diversification in Yamuna Doab‖. The Geographer. 2008, Vol. 55 No.1, India. Programmes on Financing Agriculture‖, Reserve pp. 10-21. Bank of India, College of Agricultural Banking, Pune, www.connectjournals.com/file_full_text/314401H_02- 2007. 10-21.pdf http://www.cab.org.in/Lists/Knowledge%20Bank/Attac hments/9/Cropping%20%20Patterns%20and%20Diversi [18] Uttar Pradesh, ―A Rainbow land‖, 2012, pp. 1-61. fication_11_13_2007.pdf http://www.ibef.org/download/Uttar-Pradesh- [7] N. Mahmood, B. Ahmad, S. Hassan and K. Bakhsh, 260912.pdf ―Impact of temperature and precipitation on rice [19] Dainik Jagran, ― ki Hunkaar‖, News productivity in rice-wheat cropping system of Punjab Paper, 20 January 2008, Meerut Edition. province‖, J. of Animal & Plant Sciences, 2012, Vol. [20] Harit Pradesh, 22, No.4, pp. 993-997. http://en.wikipedia.org/wiki/file:India_Harit_Pradesh_lo [8] P.S. Ranade, ―Impact of Climate Change on cator_map.svg Agriculture‖, ISPRS Archives XXXVIII-8/W3 Workshop Proceedings: Impact of Climate Change on [21] International Institute for Population Sciences, ―District Agriculture, December, 2009,pp.183. level household and facility survey (DLHS-3)‖ 2007- http://www.isprs.org/proceedings/XXXVIII/8-W3/ 08:India.UttarPradesh: , IIPS, 2010. [9] Global warming news, [22] R.L. and A.V.M. SubbaRao, ―Atlas of Cropping http://www.ccchina.gov.cn/en/newsinfo.asp?Newsi Systems in India‖, PDCSR Bulletin No.2001-02, 2001, d=33875 pp.96. [10] P.S. Roy, S. Agarwal, Y. Shukla and P.K. Joshi, ―Land [23] Crops agriculture statistics, cover mapping using SPOT-VEGETATION for South http://www.apy.dacnet.nic.in/crop-fryr-toyr.aspx Central Asia‖, Book chapter, Indian Institute of Remote [24] IMD district-wise monthly rainfall data : 2004-2008, Sensing (IIRS), Dehradun, ISBN: 81-901418-5-6, 2004. http://www.indiawaterportal.org/node/7185 bioval.jrc.ec.europa.eu/products/glc2000/products/I [25] Rainfall and minimum-maximum temperature data ISSN No: 2319-3484 Volume 2, Issue 3, May 2013 44 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com

1901 to 2002, from the C.C.S. University, Meerut, Uttar Pradesh, 2008. http://indiawaterportal.org/met_data/ Currently, He is Research Associate at Project Directorate for [26] Meteorological data: 2007-2011, Farming Systems Research, Meerut. His research areas include http://www.imd.gov.in/section/hydro/distrainfall/up. Remote sensing & GIS, Eco-Physiology, Agriculture & Soil. He html has written five chapters in three textbook: Environmental [27] District-wise monthly rainfall IMD 2004-2011 Pollution & Biodiversity; Advances in Agriculture & Ecology http://www.indiawaterportal.org/taxonomy/9/Indian and Environmental Health & Problems, Discovery Publishing -Meteorological-Department. House, , 2012. And one chapter had written in a book: [28] S. Agarwal, P.K. Joshi, Y. Shukla, and P.S. Roy, Natural Resources Engineering and Management & Agro ―SPOT VEGETATION multi temporal data for Environmental Engineering, Anamaya Publishers, New Delhi, classifying vegetation in south central Asia‖, Current 2005. Dr. Avadhesh Kumar Koshal may be reached at Science, 2003, Vol. 84 No.11, pp.1440-1448. [email protected] . [29] VEGETATION users guide,1999. http://www.spotimage.fr/data/images/vege/VEGETAT/ book_1/e_frame.htm [30] M. L. Parry, T. R. Carter and N. T. Konjin, ―The impact of climatic variations on agriculture: Assessments in cool temperate and cold regions‖. Kluwer Academic Publishers. Dordrecht, 1988a, pp. 513-614. [31] M. L. Parry, T. R. Carter, and N. T. Konjin, ―The impact of climatic variations on agriculture: Assessments in semi-arid regions‖. Kluwer Academic Publishers.Dordrecht, 1988b, pp. 513-614. [32] H.S. Mavi, ―Using productivity indices for mapping efficient crop regions‖. Geo-spatial Information in Agriculture. The Regional Institute Ltd. Conference Publ. 2001. http://www.regional.org.au/au/gia/26/850mavi.htm [33] M. Singh, K.K. Dhingra and M. S. Dhillon, ―Efficient crop zones of India based on productivity indices‖.

1995. (Personal Communication). [34] S.B. Cheema, G. Rasul, G. Ali & D.H. Kazmi, ―A comparison of minimum temperature trends with model projections‖,. Journal of Meteorology. 2011, Vol. 8, No.15, pp. 39-52. http://www.pmd.gov.pk/rnd/rnd_files/vol8_issue15.htm [35] A.K. Koshal, ―Spatio-temporal SPOT VGT image analysis for crop mapping in India‖, International Journal of Scientific and Research Publications. 2012, Vol. 2, No. 11, pp. 1-10. [36] B.A, Parthasarathy, A. A. Munot and D.R. Kothawale, ―Regression model for estimation of foodgrain production from summer monsoon rainfall‖, Agricultural and Forest Meteorology, 1988. Vol. 42, pp. 167–182. [37] S. Gadgil, ―Climate change and agriculture - an Indian perspective. In climate variability and agriculture‖, Abrol, Y. R., Gadgil, S., Pant, G. B. (eds). Narosa: New Delhi, India; 1996, pp. 1–18.

Biography AVADHESH KUMAR KOSHAL received the M.Sc. degree in Botany from the Department of Botany, C.C.S. University, Meerut, Uttar Pradesh in 1998 and the Ph.D. degree in Botany ISSN No: 2319-3484 Volume 2, Issue 3, May 2013 45