Changes In Rice Area And Production Biology Essay
The aim of this survey was to look into the alterations in rice countries in the most recent decennary ( 2000-2009 ) utilizing distant feeling technique to understand its relation with rice production and rice monetary values. The survey was conducted in Nepal with an country of 14,718,100 hectares in South Asia.
The MODIS ( MOD09Q1 ) 500m time-series informations and the spectral matching techniques ( SMTs ) were found ideal for agricultural cropland alteration sensing over big country and provided fuzzed categorization truths between 63.3-100 % for assorted rice categories and truth is 79.1 % field secret plan informations. The MOD09Q1 derived rice countries for the territories were extremely correlated with the national statistics informations with R2-values is 0.9832. Dynamic mapping rice country for the most recent decennary ( 2000-2009 ) has been done by placing 8 chief categories of rice ( irrigated, rainfed, upland etc. ) . There is no important alteration in the rice country and proportionate country under different rice categories except in the twelvemonth 2006.
During the twelvemonth 2006, rice country in Nepal declined by 14 % from 2005 and 2007 figures. Area under rainfed rice category continues to be the the prevailing one from 2000 to 2009.A multi-level analysis was carried out by uniting distant feeling techniques with socioeconomic information and national statistics. The analysis highlights the kineticss of rice countries, production and increasing rice monetary values in Nepal over the old ages ( 2000-2008 ) . The findings infer that future rice research plan in Nepal demands to progressively concentrate on proper rice production planning and targeting of engineerings along the 8 chief rice categories ( ecosystems and production systems ) identified by this survey.Cardinal words: Rice maps, rice production, land usage alteration, rice categories, rice monetary value, and output.
1.0 Background/Introduction
Nepal is an agricultural state where agribusiness is still the individual largest sector in the economic system, accounting for 32 per centum of GDP ( MoF, 2008 ) . The state ‘s chief agricultural production includes paddy rice, corn, wheat, sugar cane, veggies, murphies, pulsations and tea, milk, meat and other farm animal merchandises. The state besides produces hard currency harvests like tea, ginger, citrous fruit, non-timber wood merchandises in the Hill part both for domestic ingestion and exports. Croping form is dominated by three harvests, rice, corn and wheat accounting for over 90 % of the country and nutrient grain production. Growth in agribusiness has direct impact on the national economic system and support of the hapless as more than two-thirds of the labour force is presently employed in agribusiness.
Rice is the most of import nutrient harvest in the state in footings of country, production and support of the people. It is presently grown in half of the sum cropped country and contributes more than half of the entire nutrient grain production in the state ( MoAC, 2008 ) . It besides supplies about 40 % of the nutrient Calories intake for the people of Nepal. The harvest is grown extensively under a broad scope of agroecological conditions from lowland in Terai ( 50 msl ) to high mountain vale and mountain inclines ( 2830 msl ) in Jumla- the highest height of rice turning location in the universe. The harvest is cultivated in all agro-ecological parts ( Mountains, Hills and Terai ) covering mountain inclines, hill patios, intermountain basins, river vales, and level lowland fields surrounding to India ( Gauchan et al, 2008 ) . About two-third of ( 74 % ) of Paddy is produced in the level lowland of Terai and the remainder ( 26 % ) in the hills and mountains ( Gauchan and Pandey, 2010 ) . Rice is chiefly cultivated during wet monsoon season ( June-November ) in major portion of the state. However, in some parts of the lowland fields and vale floors, they are besides grown during spring season ( Chaite rice ) as an irrigated harvest.
Transplanting is the major method of rice constitution in both the irrigated and rainfed lowland conditions. Direct seeding ( airing ) is chiefly practiced for the highland rice ( Ghaiya ) in highland Fieldss.Rice has particular significance and economic importance in agricultural development and poorness decrease in Nepal. However, we lack equal apprehension and information about recent alterations in rice country over the old ages to plan appropriate production planning and engineering aiming in the state. Therefore, this survey aims to analyse alterations in rice country in the most recent decennary ( 2000-2009 ) utilizing distant feeling technique to understand its relation with rice production and monetary values.Surveies describing advantages of MODIS orbiter imagination to map agribusiness alteration response to H2O handiness in the big countries between water-surplus twelvemonth water-deficit old ages and to understand the alteration kineticss of irrigated countries due to fluctuating H2O handiness ( Gumma et al. 2009, Gaur et Al. 2008 ) .
Satellite imagination can supply elaborate maps of where cropping forms change significantly in response to H2O handiness ( Thiruvengadachari et al. 1997 ) . Satellite imagination has been progressively used to quantify H2O usage and productiveness in irrigation systems ( Bastiaanssen and Bos 1999 ; Thiruvengadachari and Sakthivadivel 1997 ) , but has less often been used to place how rainfed rice countries change in response to fluctuations in clime alteration.
Given the above background, the chief aim of this research was to analyze the rice turning countries change response to over last decennary and to understand the alteration kineticss of rice turning countries due to climate alteration. A multi-level analysis ( farm degree studies and regional rice countries and production appraisal through distant feeling techniques and national statistics ) was carried out to analyze the kineticss of rice countries, production and rice monetary values in the survey country over the most recent decennary ( 2000-2009 ) .The paper is organized as follows. Following debut, it provides beginnings of informations and discusses about methods for analysing alterations in rice country utilizing distant feeling techniques. A general overview of rice production planning and targeting is presented uniting distant feeling technique with socioeconomic informations and rice production and growing rate analysis. Finally, the paper presents about consequences, treatment and decision.
2.
0 Study country:
Nepal is a landlocked Himalayan state located in South Asia, runing from 800 Tocopherol to 920 E and 260 N to 300 N ( Figure 1 ) . It is bordered to north by China ( Tibet ) and to south, east and west by India with entire geographical country of 147,181 kilometers and population of 2.7 million.
For a little district, the Nepali landscape is uncommonly diverse, runing from the humid Terai in the South to the exalted Himalayas in the North. Nepal self-praises eight of the universe ‘s top 10 highest mountains, including Mount Everest on the boundary line with China. Geographically, the state is divided into three ecological parts: Mountain, Hill and Terai suiting 7.3 % , 44.3 % and 48.4 % of the population ( CBS, 2001 ) . Administratively, the state is divided into14 zones and 75 territories, grouped into 5 development parts which are eastern, cardinal, western, mid-western and far-western parts ( Fig 1 ) .
The state has three major rivers viz ; Koshi, Gandaki and Karnali that are originated in the northern cragged portion straight from Himalayas and flow to the South. In add-on, Mahakali river besides flows from the far-western portion of Nepal to the South and acquire into the India.These rivers are the major beginning of Ganges in India.
Figure1: Study country: Map of Nepal, demoing development parts with major rivers across Nepal. River web delineated from SRTM DEM.Insert Figure 1 here
3.
0 Datas
3.1 MODIS surface coefficient of reflection informations
The MODIS 8-day composite surface coefficient of reflection merchandise from the Terra platform ( MOD09A1 ) is ideal for supervising flora at a Continental graduated table ( Thenkabail et al. 2005 ) . The seven sets of coefficient of reflection informations ( Table 1 ) at a declaration 500m, coupled with a high-repeat frequence, can capture the seasonal fluctuations in flora energy, dirt and flora wet, and surface H2O that characterize cardinal phases of rice cultivation.
The coefficient of reflection informations undergo several pre-processing stairss, including algorithms for atmospheric rectification. Furthermore, the rate of observation coverage, the sing angle, cloud or cloud shadow coverage, and aerosol lading are all assessed on a pixel-by-pixel footing to guarantee that each pel contains the best observation during that 8-day period. MOD09A1 besides includes two quality assessment datasets at the pel and set degree, which are critical for user post-processing to place and take countries of relentless cloud and snow screen. The MOD09A1 ( Version 005 ) informations are available in a tile system, where each tile covers 10 grades by 10 grades ( 1111.2 x 1111.2 kilometers at the equator, once more frequently rounded up to 1200 ten 1200 kilometer in the literature ) . We downloaded 12 tiles, for every 8 yearss, from hypertext transfer protocol: //modis-land.gsfc.
nasa.gov covering South Asia for all day of the months start from June 2000 to May 2010, which includes the rice harvests in two seasons ( kharif and rabi ) for each twelvemonth. Analysis has done for whole South Asia, nowadayss study concentrate on Nepal.Table 1: MODIS informations sets ( 7 sets ) : MODIS Terra 7-band coefficient of reflection informations features used in this survey.MOD09A1 productA?BandsA?Band breadth ( nmA? )Band centre ( nmA? )Visible scopepossible application43459-479470BlueSoil/Vegetation Differences4545-565555GreenGreen Vegetation1620-670648RedAbsolute Land Cover Transformation, Vegetation Chlorophyll2841-876858NIR1Cloud Amount, Vegetation Land Cover Transformation51230-12501240NIR2Leaf/Canopy Differences61628-16521640SWIR1Snow/Cloud Differences72105-21552130SWIR2Cloud Properties, Land PropertiesNote: 1 = of the 36 MODIS sets, the 7 sets reported here are specially processed for Land surveies.2 = MODIS sets are re-arranged to follow the electromagnetic spectrum ( e.
g. , bluish set 3 followed by green set 4 ) .3 = nanometres.
A
A
A
4 = taken from MODIS web site ( http: //modis-land.gsfc.nasa.gov/ )Insert Table 1 here
Field-plot informations
Two field-level studies were conducted during October 11-26, 2003 and August 30- September 28, 2005 across 1,004 locations covering the major cropland countries in South Aisa ( Gumma et al, 2010 ) .
The locations were chosen based on the cognition of local agricultural extension officers to guarantee that the same harvests were grown during the 2000-01 as were observed during the study. The local experts besides provided information on harvest calendars, cropping strength ( individual or dual harvest ) , and per centum canopy screen for these locations from their recorded informations for the old ages 2000-01. Overall, 1,004 spatially well-distributed informations points were collected. Of this, 75 % of the points ( 743 ) were used for call designation and labeling and the remainder of the points ( 261 ) were used for truth appraisal.
3.3 Secondary informations sets
3.3.
1 Socio economic informationsSocioeconomic informations from assorted beginnings in Nepal and international bureaus were besides used. National degree rice monetary value informations was obtained from published authorities beginnings ( Economic study 2008-09 ) and rice production and output informations were obtained from Statistical Information on Nepali Agribusiness from Ministry of Agriculture and Cooperatives ( MoAC, 2009 ) Government of Nepal. The rice monetary value in US $ was for rice was derived from FAOSTAT database 2010.
3.
3.2 SRTM 90 m lift
The Shuttle Radar Topography Mission ( SRTM ) obtained lift informations on a near-global graduated table to bring forth the most complete high-resolution digital topographic database of Earth ( Farr and Kobrick, 2000 ; Rabus et al. , 2003 ; Rodriguez et al. , 2005 ; Farr et al. , 2007 ) . Since the topography of the river basin under probe is extremely diverse, the SRTM lift informations set is utile in dividing irrigated countries within the bid countries and deltas with low lifts and high elevated countries with forest flora.
The SRTM informations ( 90 m resampled to 30 m ) were besides used to execute image cleavage based on lift values in the basin.
3.3.3 National statistics for rice countries:
Rice country statistics were obtained at the sub-national degree ( development parts, zones and territories ) , and stand for the entire cropland country sown to rice. The information adopted by the Statistical Information on Nepali Agribusiness from Ministry of Agriculture and Cooperatives, Nepal ( MoAC, 2009 ) and the Central Bureau of Statistics, Government of Nepal ( CBS, 2008 ) . The information was besides supplemented by Economic Survey of Nepal ( MoF, 2009 ) .
4.0 Methods
4.1 Remote Sensing methodological analysis for historical rice maps
MODIS ( MOD09Q1 ) informations were used to map rice countries from 2000 to 2009 harmonizing to the methodological analysis adopted from Gumma et Al, ( 2010 ) .
The basic procedure begins with downloading eight-day complexs of MOD09QNDVI with 500m declaration were stacked into a individual information set for 2000-01 ( 43 cloud free NDVI images ) , 2001-02 ( 43 cloud free NDVI images ) , 2002-2003 ( 42 cloud free NDVI images ) , 2003-04 ( 36 NDVI images ) , 2004-05 ( 42 cloud free NDVI images ) and 2005-2006 ( 45 cloud free NDVI images ) , 2006-07 ( 42 cloud free NDVI images ) , 2007-08 ( 41 cloud free NDVI images ) , 2008-09 ( 44 cloud free NDVI images ) and 2009-10 ( 37 cloud free NDVI images.The MODIS mega-file was divided into three distinguishable zones, one was major irrigation bid countries zone utilizing India ‘s cardinal board of irrigation and power ( CBIP ) command country. The thought behind the cleavage procedure is to concentrate more on the sections holding higher sum of informal and disconnected irrigated categories such as the CBIP bid countries. Such sections would be classified in to more Numberss of categories so the other for better word picture of different irrigated categories ( irrigated rice individual harvest, irrigated rice double harvest, irrigated-conjunctive usage and etc ) utilizing protocols explained in Gumma et al. , 2010. Second was SRTM- derived incline zones, Such cleavage allows for easier category spectrum separation and designation of minor irrigated countries ( informal irrigated countries ) other than command countries and deltas with low lifts and high elevated countries with forest flora.Each dataset is so classified utilizing unsupervised ISOCLASS bunch K-means categorization. Unsupervised categorization was used alternatively of supervised categorization in order to capture the scope of variableness in phenology over the image.
At a regional graduated table and where the NDVI signatures of all possible categories are non known, unsupervised categorization captures the scope of phenological variableness. The figure of categories varied from 40 to 100 based on country covered by the section and complexness of country.Class designation and labeling is based on bi-spectral secret plans, NDVI time-series secret plans, land truth informations and really high declaration images ( Google Earth ) . Grouping category spectra based on category similarities and/or by comparing them with ideal/target spectra, strict protocols for category designation and labeling that included usage of big volumes of groundtruth informations and the usage of really high declaration imagination.
Deciding assorted categories through stipulating GIS spacial analysis/Modeling beds ( DEM/Rainfall ) , and set uping advanced methods for irrigated country computations and truth appraisals. The categories generated from the unsupervised categorization were aggregated into 11 categories and named based on spectral similarity and intensive field secret plan information. Spectral matching ( Thenkabail et Al, 2007 ) was used to associate the categories for all old ages. These procedures are described in Thenkabail et Al, 2007 and Gumma et Al, 2008 briefly.
Once the rice countries are mapped, calculate precise countries, nevertheless, in coarser declaration MODIS were 500m on a side, which was larger than many agricultural Fieldss in the survey country. Many pels overlapped several land screen types, so any given land screen category had a corresponding value for the areal fraction that was rice harvest ( Gumma et al. , 2010 ) . The rice fractions were determined utilizing intensive field secret plan information.Figure 2: Overview of the methodological analysis for rice production planning and targeting.Insert Figure 2 here
4.
2 Accuracy assessment base on field-plot informations
A fuzzed truth appraisal was performed utilizing 25 % ( 261 informations points ) of field-plot informations to deduce robust apprehension of the truths of the datasets used in this survey. The field-plot informations were based on an extended field run conducted throughout India during kharif season by the International Water Management Institute research workers and consisted of 1,004 points. Fuzzy accuracy appraisal provides realistic category truths where land screen is heterogenous and pixel sizes exceed the size of unvarying land screen units ( see Gopal and Woodcock 1994 ; Thenkabail et Al.
2005, 2009 ; Gumma et Al. 2009 ) . For this survey, we had assigned 3 ten 3 cells of MODIS pels around each of the field-plot points to one of six classs: perfectly correct ( 100 % correct ) , mostly right ( 75 % or more correct ) , correct ( 50 % or more correct ) , wrong ( 50 % or more incorrect ) , largely wrong ( 75 % or more incorrect ) , and perfectly wrong ( 100 % incorrect ) . Class countries were tabulated for a 3 ten 3-pixel ( nine-pixel ) window around each field-plot point.Based on the theoretical description given by Congalton and Green ( 1999 ) , equations 1, 2, and 3 were used to gauge truths and mistakes. Field-plot information was used to find robust truths, utilizing equations 1, 2, and 3.where RFPCIA = rice field-plots classified as rice countries ( figure ) , A TRFP = entire rice field-plots ( figure ) , NRFPRA = non-rice field-plot points classified as rice country ( figure ) , TNRFP = entire non-rice field-plots ( figure ) A , and RFPNRA = rice field-plots classified as non-rice countries ( figure ) .
4.
3 Accuracy appraisal based on correlativities between nose count and orbiter informations
The concluding classified map of rice countries was compared against rice country statistics for Nepal state at the highest available spacial declaration. The MODIS rice country fractions were aggregated to acquire a summed rice country per territory or province and these were compared against the reported deep-rooted countries for the kharif season in 2000-01.
4.4 Growth rate analysis for country, production and output
The growing rate was computed for each period by the least-squares arrested development technique in STATA statistical bundle by reassigning country, production and output values into natural logarithm.The least-squares growing rate, R, is estimated by suiting a a least-squares tendency arrested development to the logarithmic one-year values of the variable in the relevant period ( WDR, 2008 ) . The arrested development equation takes the signifier log Xt = a + bt + vitamin E and and represents a logarithmic transmutation of the compound growing rate equation: Ten t. In these equations, X is the variable of involvement, t is the clip ( twelvemonth ) , and a = log X T = X0 ( 1+r ) 0 and b = log ( 1 + R ) are the parametric quantities to be estimated ; e is the error term.
If b* is the least-squares estimation of B, so the mean one-year per centum growing rate, R, is obtained as [ antilog ( b* ) ] – 1 and is multiplied by 100 to show it as a per centum.
4.5 Rice monetary value, country and output comparing
National mean rice monetary value and rice output obtained from national functionary beginnings and FAOSTAT was presented diagrammatically to understand their tendency, variableness over the old ages and relationship between monetary value and output. Rice country obtained from distant detection was besides compared with rice monetary value for the last one decennary to detect form of rice country and monetary value fluctuation and tendency over the old ages.
5.
0Results
In this subdivision, we focus on the ensuing rice categorization over old ages, the categorization truth appraisal based on field-plot informations, comparing between the MODIS rice countries estimations, sub national statistics, growing rate in rice country with output and production, rice country and production by developmental parts, tendency in rice country and monetary values and Relationship between paddy output and monetary value.
5.1 Rice map and country statistics
Wholly, 11 categories were identified and labeled ( Figure 3 ) . Almost 1.7 million hectares of cultivable land were labeled as incorporating some grade of rice cultivation based on FPAs.
However, when RAFs ( last column in Table 4 ) were used, the existent ( sub-pixel ) countries were 1.6 million hectares ( last column in Table 3 ) for the twelvemonth 2000-01. The concluding category name or label ( Figure 3, Table 3 ) is based on the predomination of a peculiar rice category ( e.g. , single- or double-season rice ) , and the dominant H2O beginning.
For illustration, the name for category 1 is “ 03. Irrigated 100 percent – Rice/other ” . This rice category is dominated by rice cultivation in the moisture and summer seasons and the country is preponderantly irrigated from surface H2O beginning. This category occurs scattered in the different parts both in the lower Hills and Terai. The largest country under this category are found in cardinal and western Hills, Terai and interior Terai vale. Similarly, category 9 is labeled “ 09.
Rainfed 100 per centum – Rice ” since this is an intensely cropped rice category, but to a great extent dependent on seasonal rains. This category is preponderantly scattered across east to far-wetern part in heavy rainfall countries including in higher lift such as hilly patio. Similarly, category 2 is “ 02. Irrigated 100 percent-rice-rice or rice-other harvest ” , preponderantly in the Terai part from east to far-western part around Indo-Gangetic s basins ( along India lodger ) . The spectral separability in the temporal NDVI signatures for each of the rice categories is shown in ( Gumma et al. ( homework ) ) .The categorization truths from the 261 field-plot observation informations points in the proof dataset are summarized in Table 3. The fuzzed categorization truth varied from 63.
3 % to and 100 % across 12 categories with an overall truth of 79.1 % i ?nearly four out of five rice pels have been right classified as rice. It has to be noted that the uncertainness of approximately 20 % is due to the inter-mix among the assorted rice categories. So, that the rice versus no-rice category truth will be really high. The irrigated categories, by and large, have higher categorization truths than the rainfed or assorted irrigated/rainfed categories of assorted categories ( Table 3 ) .
Of the 261 points, 61 % of the points were classified as perfectly correct.Insert Figure 3 hereInsert Table 2 hereInsert Table 3 hereFigure 3: Rice categorization with beginning wise across the Nepal over the old ages ( get down from 2000-01 to 2009-10 ) .Table 2: Rice countries part wise for NepalRegionRice country ( hour angle )Year 2000Year 2001Year 2002Year 2003Year 2004Year 2005Year 2006Year 2007Year 2008Year 2009Cardinal499,627482,132465,509480,312455,658465,802405,998469,411466,504467,792East500,399483,987469,333489,138470,835472,669390,289474,678471,908470,640Far-Western160,119166,255159,474164,551160,079160,157140,712158,967159,728158,245Mid-Western165,258154,522145,936157,236151,632152,179131,503150,774151,945152,764West304,295302,109294,955304,154296,320296,535259,815295,111295,910295,891Entire rice country1,629,6991,589,0041,535,2071,595,3901,534,5251,547,3411,328,3161,548,9411,545,9951,545,332Table 3: Fuzzy truth appraisal from field-plot informations. Valuess in the tabular array indicate the % of field-plot Windowss in each category with a given rightness % age.
A
A
Fuzzy categorization truth
A
Sample sizeSum CorrectSum Incorrect( perfectly correct )( largely correct )( correct )( incorrect )( largely incorrect )( perfectly incorrect )Rice category figure and category name( 100 per centum correct )( 75 per centum and above correct )( 51 per centum and above correct )( 51 per centum and above incorrect )( 75 per centum and above incorrect )( 100 per centum incorrect )
A
( per centum )( per centum )( per centum )( per centum )( per centum )( per centum )( per centum )( per centum )01. Irrigated 100 per centum – Rice/Rice2492.67.48121061102.
Irrigated 100 per centum – Rice/Rice or Rice/Other2386.313.775012112003. Irrigated 100 per centum – Rice7582.217.87157105304.
Irrigated 60 per centum / Rainfed 40 per centum – Rice/Rice248.551.5261753271205. Irrigated 30 per centum / Rainfed 70 per centum – Rice/Rice or Rice/Other6788.211.96202574106. Upland 80 per centum / Rainfed 10 per centum / Irrigated 10 per centum – Rice2488.
012.07511293007. Rainfed 60 per centum / Irrigated 40 per centum – Rice/Rice385.714.3551714301208. Rainfed 90 per centum / Irrigated 10 per centum – Rice2077.622.
457912105809. Rainfed 100 per centum – Rice2063.237.162014191410.
Deepwater 100 per centum – Rice/Rice163.338.7451810122511. Deepwater 100 per centum – Water/Rice184.338.7551911041212. Wetlands 100 per centum – Rice/Rice189.011.
06910100110
Entire
261
79.1
20.9
60.9
8.9
9.
2
6.8
6.0
8.
2
5.2 Accuracy appraisal based on comparing with sub-national statistics
Figure 4 compares the summed rice countries across all categories against the published rice statistics. We performed the comparing at the territory degree ( 75 spacial units ) across survey country, but, for grounds of infinite, we merely report the tabulated countries at the development parts level ( 5 spacial units ) table 2 and the territory degree comparing is shown in the figure 4. Figure 4 shows the relationship between MODIS country summarized at territory against the subnational rice countries at the same degree of spacial item. The degree of understanding between the MODIS country estimations and the published statistics is really goodi ? 98.3 % at the territory degree ( Figure 4 ) .Figure 4: Accuracy appraisal and proof.
The district-wise rice countries derived utilizing MODIS 500m compared with agricultural nose count informations.Insert Figure 4 here
5.3 Growth rate in rice country, output and production
The analysis of one-year growing rate for rice country, output and production for the last one decennary ( 2000-2008 ) is presented in Table 4. Overall analysis of the growing rate for the last 9 old ages indicated that there was no statistically important output and production growing in the state. The country growing was negative but non-significant. However, when analysis was made by spliting the entire period into three sub-periods for each of three old ages, it showed fluctuation over the different bomber periods. Output and production growing was negative in the first three twelvemonth period ( 2000-20002 ) .
Area growing was besides negative but statistically non-significant. In the 2nd three twelvemonth period ( 2003-2005 ) , yield growing was negative and statistically important. In the last and most recent three twelvemonth period ( 2006-2008 ) , per annum output growing was high, positive and statistically important but country and production growing was non important despite their positive growing. Overall, in the last decennary, the growing in rice production and output is stagnated and non turning in a rate of population growing ( 2.2 % per twelvemonth ) .
Table 4: Growth rates ( % per twelvemonth ) of rice country, output and production in NepalTime periodAreaOutputProduction2000-2002-0.61-1.0*-1.0*2003-20050.
11-2.96**-2.842006-200837.2*10
All ( 2000-2008 )
-0.220.370.14*** , ** , * indicate statistical significance at 1 % , 5 % and 10 % chance degree severallyInsert Table 4 here
5.4 Rice country and production by Developmental Regions
Rice is the chief beginning of support in all the five development parts of Nepal.
The largest country and production of rice takes topographic point in eastern part followed by cardinal and western part ( Table 2 ) . Mid-western and far-western parts have comparatively smaller country and production. Western part is intermediate among them accounting for 20 % of the country and production portions. Eastern and cardinal part each history for 30 % of the country and production portions in the state, whilst mid-western and far-western parts account for less than 10 % of country and production portions.
Output degrees are besides higher in eastern and centeral parts and lowest in far-western part.Table 5: Average of rice country, output, production and per centum portions by parts ( 2000-2008 )Development partRice countryProduction*Yield*( ‘000 hour angle )Percentage( ‘000 Mt )Percentage( t/ha )Eastern47030.41,34631.92.
85Cardinal46730.21,27930.32.73Western29819.
482119.52.55Mid-Western1489.64199.92.74Far-Western16110.
45238.42.18All Nepal1,5471004,2191002.72*Source: Area from distant feeling estimations and production and output from MoAC ( 2008 )Insert Table 5 hereRice country and production are lowest in Mid-Westerna nd Far-Western development part. Output degrees are besides lower than national norm in these region.A recent national life standard study of Nepal ( NLSS, 2004 ) besides indicate that poorness rate is highest in these Mid and Far-Western parts.
There is positive relation between poorness incidence and rice country and production degree bespeaking a demand of aiming rice engineerings development and publicity in these destitute parts of Nepal..
5.5 Trend in rice country and monetary values
The relationship between tendency in rice country and paddy monetary value ( US $ ton/ha ) is presented in Fig 5. The rice country remained about changeless over the old ages. However paddy monetary value ( unsmooth rice ) is demoing increasing tendency over the recent old ages peculiarly after 2001.This indicates the low supply and increasing demand for rice in the recent old ages due to stagnant production and increasing population force per unit area and urbanisation.
Increase monetary value of rice has negative impact on the nutrient security, nutrition and the support of the low income groups shacking both in rural and urban countries. Land less labourers, fringy husbandmans, adult females and those who have limited income chances are going most vulnerable from increasing paddy monetary values as they spend big portion of their income on rice nutrient entirely. Poorer people tend to cut down consumption of nutrient as a consequence of addition of nutrient monetary values, peculiarly harsh rice which is a major basic of the poorer group ( WFP, 2008 ) .Figure 5: Tendency in rice country and paddy monetary value in NepalInsert Figure 5 here
5.6 Relationship between paddy output and monetary value
Paddy monetary value is steadily increasing in the recent old ages whilst the output of rice remained variable over the old ages ( Fig 6 ) . A high variableness in rice output over recent old ages is caused by variableness of the rainfall form. The output degree ( 2.5 t /ha ) was lowest in 2006 due to drought conditions predominating peculiarly in major rice bring forthing parts of eastern and cardinal Terai ( FAO, 2007 ) .
In recent old ages ( in 2008-09 ) the output has regained making historic high at 2.9 t/ha for Nepal. However, paddy monetary value is steadily increasing over the recent old ages bespeaking a demand for increasing production and productiveness of the harvest by proper planning and targeting of the technologies..Figure 6: Tendency in paddy monetary value and output in NepalInsert Figure 6 here
6.
0 Discussion and Conclusion
This survey demonstrated a suite of methods and attacks for rice countries alterations over the old ages and its impact on production and monetary values. First, a baseline rice map of Nepal was produced for last 10 old ages ( start from 2000 to 2009 ) and their countries calculated. A fuzzed categorization truth showed that the 9 rice categories were mapped with absolute truth runing from 63.2 % to 92.
6 % and an overall categorization truth of 79.1 % for all the 9 categories. Almost all of the inter-mixing was between two rice categories.
Second, the truth was besides determined by correlating the MODIS-derived rice countries with sub-national statistics obtained from Nepal agribusiness section. For this, the R2 values were 96.8 % at the territory degree and 98.32 % at the province degree for 2000-01. Dynamic mapping rice country for the most recent decennary ( 2000-2009 ) has been done by placing 8 chief categories of rice ( irrigated, rainfed, upland etc. ) .
There is no important alteration in the rice country and proportionate country under different rice categories over the old ages except in 2006. During the twelvemonth 2006, rice country declined by 14 % from the 2005 and 2007 figures. Area under rainfed rice category continues to be the the prevailing one from 2000 to 2009.
Hence the growing in rice production and productiveness has stagnated over the recent decennary.Rice monetary value is increasing steadily since last one decennary, nevertheless, country and production growing remains dead. Furthermore, rice outputs are variable over the old ages and are non turning late in similar gait with rice monetary values and state ‘s population. Consequently, the state is confronting increasing shortage of rice for run intoing its nutrient security demands and cut downing poorness. Poor consumers and little holder husbandmans ( who are besides consumer of rice ) are progressively passing higher portion of their income to run into their nutrient demands as a consequence of addition monetary values of rice in the market in recent old ages. This necessitates scientific rice production planning and targeting of suited new engineerings to heighten rice production and productiveness in the different rice categories ( ecosystems and production systems ) as generated from distant feeling analysis and indicated in this paper.
Rice research plan in Nepal demands to progressively concentrate on development and airing of emphasis tolerant rice engineerings and associated harvest direction patterns for rainfed environments to cut down production and output variableness over the old ages and besides cut down the negative impact of drouth and other abiotic and biotic emphasiss. Differences in mark sphere features and technological demands should be accounted for rice engineering development and publicity.
