01SPATIO-TEMPORAL DIFFERENTIATION AND FUTURE TRENDS ANALYSIS OFAGRICULTURAL PRODUCTIVITY IN HATHRAS DISTRICT, UTTAR PRADESH
Mumtaj Ahmad1, Pasarul Islam2 and Enayat Bano3
1
Assistant Professor, Department of Geography,
Aligarh Muslim University (Central University), Aligarh, India. Email: mumtazgeog@gmail.com
2
Post-Doctoral Fellow, Indian Council of Social Science Research, Ministry of Education,
Government of India, New Delhi, India. Email: islam.pasarul@gmail.com
3
Reseacher, Department of Geography, Aligarh Muslim University (Central University),
Aligarh, India. Email: enayatkhan.khan@gmail.com

Abstract: The agriculture sector has gone through different
How to cite this paper:
Ahmad
Mumtaj,Islam
Pasarul and Bano Enayat(2023)
Spatio-Temporal
Differentiation and Future
TrendsAnalysisofAgricultural Productivity in
HathrasDistrict,
Uttar
Pradesh, Journal of Global
Resources, Vol. 09 (02)DOI:
10.46587/JGR.2023.v09i02.001

Received: 23 Feb 2023
Reviewed: 12 May 2023
Final Accepted: 15 June 2023

phases of growth, embracing a wide variety of institutional
interventions, technology and policy regimes in India. It is
important to assess whether the past, present and future
trends of agricultural productivity are compatible with thegrowing demand of people. This paper analyzes the spatiotemporal differentiation and future trends of agricultural
productivity in Hathras district, Uttar Pradesh. This study isbased on secondary sources of data for the period 2000-01
and 2014-15. Yang’s Crop Yield Index (CYI), Standard
Deviation (SD) and Data Projection techniques have beenused in this work. The study reveals that the whole district
has made reasonable enhancements in their agricultural
productivity from 2000-01 to 2014-15 which are varied overspace and time. The future trends of agricultural productivity
for the year 2030-31 indicate that the study area hascontinuously boosted its productivity since 2000-01. It
concludes that the problems which are needed to address
are accessibility, invention, and equal delivery mechanism ofgovernment-sponsored schemes, programs, policies andprojects like irrigation, capacity building programs, farmer
sensitization issues and others.
Keywords: Agricultural Productivity, Crop Yield Index,
Differentiation, Future Trends

1Introduction
The United Nations Food and Agriculture Organization estimates food requirements will morethan double by 2050. The increasing demand for food is driven by global population growth andhigher living standards in developing economies calling for higher quality and a greater variety
of food for human consumption. This can be only possible by increasing agricultural productivity(Fischer, 2009). Productivity measures the efficiency with which inputs are converted into
outputs. Productivity performance is largely controlled by technological progress (Nossal andGooday, 2009). Agriculture has been an integral part of development in most countries.
Undoubtedly, it is the largest livelihood provider in India, especially in the vast rural areas. About
seventy per cent of the country's population directly or indirectly depends upon agriculture. It isthe central pillar to India’s socio-economic development that contributes 19.9 per cent to thetotal GDP and employs more than fifty percent of the country’s workforce (Socio-Economic
Survey 2021-22). Indian agriculture has transformed owing to scientific utilization of land as wellas governmental efforts, which significantly contributes to national development (Ahmad, 2002).
This has made us self-sufficient and taken us from being a begging bowl for food after
independence to a net exporter of agricultural products (Kumar and Sharma, 2022; Sharma,2022).
Despite high levels of production, agricultural productivity in India is lower than in otherlarge producing countries. The yield of rice has increased by more than two times since 1970
and was 40,577 kg per hectare in 2019-20. However, India’s yield of rice is low when compared
to countries such as China, Japan, Brazil, Vietnam, Indonesia and Bangladesh (FAO,
2020). The reasons for low yield per hectare as compared to developed countries are small
landholdings, illiterate farmers, unscientific use of fertilizers, lack of credit facility, less use oftechnology and poor infrastructure (Nattagh, 1986; Sharma and Sharma, 1993). It is generally
agreed that the yield per acre/hectare may be considered to represent the agricultural
productivity in a particular region and that other factors of production be considered as thepossible causes for the variation while comparing it with the other regions (Usmani, 1994).
Pandit (1983) has stated the connotation of productivity in these words, "Productivity is definedin economics as the output per unit of input the art of securing an increase in output from thesame input or getting the same output from a smaller input". productivity is a physicalrelationship between output and the input which gives rise to that output (Saxon, 1965). Stamp(1958) while attempting to measure crop productivity per unit area emphasized, that areal
differences in productivity are the result partly of natural advantages of soil, and partly of farming
efficiency. Farm efficiency refers to the properties and qualities of various inputs, how they are
combined and utilized in production. There are many different concepts of productivity and still
more ways for computing it. The productivity of agriculture so far has been looked at fromdifferent points of view, such as productivity of land, labour and capital. These are the bestknown partial productivity measures (OECD, 2008). Attention may especially be focussed on
the productivity of land because it is the most permanent and fixed among the three
conventional categories of inputs (land, labour and capital) and in recent times it has assumed
special importance with the population explosion (Anríquez and Stamoulis, 2007; FAO, 2017).
Productivity of land is obviously of primary importance in a country like India with a high densityof population. Where land resources are limited, the principal means of raising production tokeep pace with the growth of population and demand for improved diets is by raising yield per
hectare (Ahmad and Islam, 2017; Islam, 2020).

Objectives
The objective of the present study is to find out the spatial and temporal differentiation and future
trends of agricultural productivity in Hathras district of Uttar Pradesh.
2

Selection of the Study Area
The Hathras district is situated between 27°20'0'' north and 27°50'0'' north latitudes and 77°40'0''
east and 78°40'0'' east longitudes. Hathras, a newly created district (1997) of Uttar Pradesh
curved out from Aligarh, Agra, Mathura and Etah are located in the western part of the state. It
covers an area of 1840 square km in the state of Uttar Pradesh. The total population in the
district is 15,64,708 out of which 12,32,015 are living in rural areas and 3,32,693 are in urban
areas. A total of 71.52 per cent of people are literate, of which 83.48 per cent are male and57.74 per cent are female in the study area. Nearly 90 per cent of areas of the district have been
devoted to agriculture activities (Chandramouli and General, 2011; Agriculture Census, 2015). The
climatic character of hot summer and dry winter with unpredictable rainfall in the monsoon
season creates a major hurdle for the farmers. This district of western Uttar Pradesh comesunder the influence of the green revolution since the 1960s. It is located in the Ganga-Yamuna
Doab region has a flat surface, fertile alluvial soil and better irrigation facilities that offer
agricultural practices throughout the year. Hathras district plays an important role in thedevelopment processes of the state. The river Ganga and Yamuna make up the east and west
borders of the district respectively. The other tributaries are Kali, Sengar and Karwar haveplayed a significant role in the fashioning of the district (District Census Handbook, 2011).

Material and Methods
Data Sources
The ongoing research work is based on secondary sources of data obtained from District
Statistical Bulletin (2001 & 2015), Census of India, Agricultural Census of India and Block
Development Offices of Hathras for 2000-01 and 2014-15.
Productivity Measurement
Though there are several measures of agricultural productivity, but the method suggested by
W.Y. Yang’s (Crop Yield Index method, 1965) is most applied and the best measure of
agricultural productivity, particularly land productivity (Shafi, 1984; Siddiqui, Rehman andSiddiqui, 1984; Munir, 1992; Ahmad, 2002; Rehman, 2003; Aktar, 2016; Islam, 2020; Liu et al.,2020; Rahaman, 2020). The present study used W.Y. Yang (1965) methods to computeproductivity because of convenience. He has used the ‘Crop Yield Index' for assessing
agricultural productivity. It considers the yield of all crops in a farm computed with the average
yield of crops in the region. Then calculate the crop yield in the farm as the percentage to theregion and the obtained value is multiplied by the area of each crop in the farm. By adding allvalues obtained by this was divided by the sum of the area occupied by each crop on the farm.
Finally, the average desired crop index is obtained for any particular farm (Table 01).
Table 01: Method of Calculating Crop Yield Index (W.Y. Yang’s 1965)
Name ofthe crop

The area under
Average Yield
Crop yield in thecrops in theDistrict Block block as the percentage
block
to the district
Rice
206
21.47
24.73
86.818
Wheat
14063
34.49
33.32
103.511
Barley
1930
27.52
27.82
98.922
Millet
6579
17.72
16.98
104.358
Maize
582
19.9
22.2
89.640
Total
23360
Computation of Crop Yield Index for Sasni Block, 2000-01
Source: Calculated by Researcher

Percentage
multiply by
area (in hectares)
17884.43
1455680.88
190918.76
686571.73
52170.27
2403226.08/23360
102.88All of the major crops grown in the district have been taken into account for calculating
crop yield index. Then, all the seventeen crops are classified into four major groups (Table 2):
3

#
1
2
3
4

Table 02: Classifications of Major Crops
Major Category
Crops
Cereal Crops
Rice, Wheat, Barley, Millet and Maize
Pulses Crops
Masur, Gram, Pea, Arhar, Urad and Moong
Oilseeds Crops
Sarson, Til and Sunflower
Cash Crops
Sugarcane, Potato and Cotton

Source: Computed by Author Based on DSB, Hathras

Future Trends Analysis
The data projection technique has been applied to predict agricultural productivity for 2030-31.
The projection formula has been used as follows:𝐧

𝐏𝐏 = 𝐏𝟏 + 𝐍 (𝐏𝟐 − 𝐏𝟏 )Where,
PP
P1
P2
N
n

=
=
=
=
=

Projected figure
Figure of the previous year
Figure of the succeeding year
Number of years between periods
Number of years between the previous years and the year for which dataor figure would be projected.

Spatial Differentiation
To analyze spatial differentiation of agricultural productivity for 2000-01 and 2014-15, theproductivity indices have been grouped into high, medium and low categories. For this, themean and standard deviation (SD) of the composite scores are calculated. Then SD is divided
by 2. Half of the SD is added to the mean of composite score to form high category and half of
the SD is subtracted from the mean to form low category and the rest of the values lying between
high and low category limit have been under the medium category. ArcGIS 10.2 software has
been used for mapping purposes.

Results and Discussion
Productivity Differentiation of Cereal Crops (2000-01 to 2014-15)
Cereals are the most dominant crop in the region. It covered an area of 1,75,066 hectares in
2000-01 which declined to 1,56,309 hectares in 2014-15, accounting 73.26 per cent and 62.86
per cent of the total cropped area respectively. The productivity region of cereals for the period
2000-01 and 2014-15 has been presented in Figures 1 and 2 respectively. In 2000-01, threeblocks namely Sasni, Sadabad and Hasayan have been recorded as a highly productive region
with an index value of more than 102.08. The index value ranges between 98.23 and 102.08
falls under the category of medium productivity region which is found in Sikandra Rao block
only. The lower productivity region lies in the blocks of Mursan, Hathras and Sahapao which
accounted for 42.86 per cent of the total blocks. On the other hand, in 2014-15, two blocks
Mursan and Sahapao have been recorded as highly productive whose value lies above 103.40.
The medium productivity regions of cereals are found in the block of Sasni and Sadabad. The
low productivity extended over the three blocks namely, Sahapao, Hathras and Hasayan having
indices value less than 99.63. The study unveils that though the number of blocks under highproductive region has been decreased but the index value is remaining high during the periodof study.

4

Figure 01: Productivity Regions of Cereals (2000-01) in Hathras District

Figure 02: Productivity Regions of Cereals (2014-15) in Hathras District

Productivity Differentiation of Pulses Crops (2000-01 to 2014-15)
Pulses include masur, gram, pea, arhar, urad and moong in the district. The area under thesecrops was 12,665 hectares with 5.30 percent of the total cropped area during the year of 200001. It can be seen from Figure 3 that during 2000-01, two blocks namely, Mursan and Sahapao
have recorded an indices value of more than 101.44 falls in the category of higher productive
region, whereas the low productive regions include three blocks namely, Hasayan, Hathras andSikandra Rao. The remaining two blocks i.e. Sasni and Sadabad have reported medium
productivity in the district. The area under pulses was reached up to 5,845 hectares in 2014-15,
which accounted for 2.35 per cent of the total cropped area in the region. It is observed
from Figure 04 that Sadabad is the only block that comes under the higher productive region ofpulses. The indices value ranges from 98.62 to 105.62 lies in the medium productive region. It
includes Mursan, Hasayan and Sikandra Rao blocks. The remaining three blocks namely,
Sasni, Hathras and Sahapao have been found under the low productive region with an indices5value of less than 98.62. It discovers that the area under pulses has been declined by 6,820
hectares during the period from 2000-01 to 2014-15. All crops under this group have foundnegative growth where the area of gram decreased by 99.47 per cent followed by pea 94.42
per cent, masur 56.52 per cent, moong 51.54 per cent, arhar 46.21 per cent and urad 28.24 percent over the year of 2000-01. The productivity regions of pulses show that there are threeblocks which recorded a decrease in productivity i.e. highest decreased in Sahapao block by
12.9 points followed by Sasni block 6.57 points and Mursan block 3.43 points, whereas theremaining four blocks namely Hathras, Sadabad, Sikandra Rao and Hasayan have accelerated
their productivity throughout the analysis.
Figure 03: Productivity Regions of pulses (2000-01) in Hathras District

Figure 4: Productivity Regions of Pulses (2014-15) in Hathras District

Productivity Differentiation of Oilseeds Crops (2000-01 to 2014-15)
Oilseeds occupy an area of 11,049 hectares which is 4.62 per cent of the total cropped area inthe region. Productivity regions of oilseeds for the period 2000-01 have been presented
in Figure 5. It ascertains that there are four blocks namely, Hathras, Mursan, Sahapao, Sadabad
and Sikandra Rao emerged in the higher productive region with an indices value of more than
94.77. The medium productivity region of oilseeds has been found in the block of Hasayan,
whereas the low productive region with indices value of less than 84.87 appears in Sasni block.
In 2014-15, oilseeds covered a total of 6,448 hectares area which represents 2.59 per cent of6the total cropped area in the district. It is observed from Figure 6 that a large proportion of the
district falls under the category of medium productive regions which includes Mursan, Hathras,
Sahapao and Sikandra Rao blocks with an indices value varying from 97.06 to 103.36. The highproductive region of oilseeds has been recorded in the block of Sadabad. The remaining two
blocks Sasni and Hasayan lie below 97.06 come under the cluster of the low productive region.
The area under oilseeds has been reduced by 4,601 hectares during the period from 2000-01
to 2014-15. But til is the only oilseed which makes remarkable enhancement in terms of bothareas as well as production in the region. It added 71 hectares of area under its cultivation,
whereas sarson decreased by 4,619 hectares and sunflower by 53 hectares over the year 200001. Similarly, Tables 3 and 4 depict that there are three blocks namely, Sasni, Sadabad, Mursan
and Sikandra Rao have raised their productivity by 15.79 points, 11.24 points, 2.15 points and0.79 points respectively. But the highest decline is observed in the Hathras block 3.18 points
followed by Hasayan block 2.88 points and Sahapao block 2.58 points during the period from
2000-01 to 2014-15.
Figure 05: Productivity Regions of Oilseeds (2000-01) in Hathras District

Figure 06: Productivity Regions of Oilseeds (2014-15) in Hathras District

Productivity Differentiation of Cash Crops (2000-01 to 2014-15)
Cash crops covered an area of 14,531 hectares accounted for 6.08 per cent of the total cropped
area of the region in the year 2000-01. It is found from Figure 7 that the highest productivity of7cash crops has been recorded in the blocks of Sadabad, Hathras and Sikandra Rao having an
indices value of more than 101.08, whereas Mursan, Sahapao and Hasayan blocks whose value
ranges between 98.36 and 101.08 seen as a medium productive region. The remaining Sasni
block falls under the low productive region in the district. Cash crops include sugarcane, potato
and cotton extended over 50,666 hectares of land which becomes the second-largest crop after
cereals, covering 20.13 per cent of the total cropped area of the region in 2014-15. It is obvious
from Figure 8 that the Sahapao block has been reported higher productivity, but a large numberof blocks namely, Mursan, Sahapao, Sikandra Rao and Sasni have been found under the
category of low productivity region with an indices value lying below 102.87. The medium
productivity records in the block of Hathras and Hasayan in the study area. The temporalvariation of cash crops productivity regions reveals that it has made considerable progress inboth areas as well as production during the period from 2000-01 to 2014-15. It is found that
Sahapao block has increased the productivity of cash crops from an indices value of 99.50 in
2000-01 to 114.62 in 2014-15, Hasayan block claimed from 98.67 to 106.02, Hathras block from
101.32 to 106.10, Sasni block from 95.64 to 97.01 and Sadabad block grown from 101.85 to
102.49. The remaining two blocks Mursan and Sikandra Rao record a sharp decrease in theirindices during the period of research.
Figure 07: Productivity Regions of Cash Crops (2000-01) in Hathras District

Figure 08: Productivity Regions of Cash Crops (2014-15) in Hathras District8

Overall Agricultural Productivity Differentiation (2000-01 to 2014-15) and Future Trends(2030-31)
The composite Yang’s crop yield index has been calculated by considering all four indices ofagriculture i.e. cereals, pulses, oilseeds and cash crops for the year 2000-01 and 2014-15 which
are represented in Figures 9 and 10 respectively. However, all the major crops come underthese major crops category in the study area, but due to the dearth of disaggregated data on
vegetables, horticulture, spices and others are not included exclusively in the study. It is evident
from Table 3 and Figure 9 that in 2000-01, the higher productive region includes Mursan,
Sahapao, Sadabad and Hasayan blocks with the indices value of more than 99.02. There are
two blocks namely, Hathras and Sikandra Rao appear as medium productive regions. The low
productive region is found in the Sasni blocks with an indices value of less than 95.97. During
2014-15, the composite index of productivity regions affirms that a small proportion of area
comes under the category of the high productivity region which includes Sadabad and Sahapao
blocks with an indices value above 101.88. A large number of blocks i.e. Mursan, Hathras,
Hasayan and Sikandra Rao have recorded medium productivity, whereas the Sasni block withan indices value below 98.37 is found under the low productivity region. The temporal variationof the composite index of productivity denotes that all seven blocks namely, Sasni, Hathras,
Mursan, Sadabad, Sahapao, Hasayan and Sikandra Rao have made reasonable improvementin their indices values during the period from 2000-01 to 2014-15.
Figure 09: Yang’s Productivity Regions (2000-01) in Hathras District

Figure 10: Yang’s Productivity Regions (2014-15) in Hathras District

9

Table 03: Yang’s Productivity Regions in Hathras District (2000-01)
Blocks
Cereals
PulsesOilseeds
Cash Crops
Composite Index
Sasni
102.88
98.19
74.97
95.64
92.92
Hathras94.39
90.54
103.02101.32
97.32
Mursan94.5
102.38100.84100.03
99.44
Sadabad105.7898.48
98.43
101.85101.13
Sahapao
96.57
107.54104.6899.5
102.07
Sikandra Rao
98.96
89.24
100.57103.81
98.14
Hasayan105.9393.04
103.8598.67
100.37
Source: Yang’s Crop Yield Index Based on District Statistical Bulletin, Hathras

Table 04: Yang’s Productivity Regions in Hathras District (2014-15)
Blocks
Cereals
PulsesOilseeds
Cash Crops
Composite Index
Sasni
100.11
91.62
90.76
97.01
94.87
Hathras
96.45
95.7
99.84
106.1
99.52
Mursan106.1
98.95
102.9998.67
101.68
Sadabad
96.75
112.62109.67102.49105.38
Sahapao107.1794.64
102.1
114.62104.63Sikandra Rao
100.22
98.67
101.36100.04100.07
Hasayan
95.87
99.61
100.97106.02100.62
Source: Yang’s Crop Yield Index Based on District Statistical Bulletin, Hathras

The future trends of agricultural productivity for the year 2030-31 are depicted in Figure
11. It has been discussed earlier that the study area has continuously boosted its productivity
since 2000-01. Similarly, it is observed that all seven blocks of the district namely, Sasni,
Hathras, Mursan, Sadabad, Sahapao, Hasayan and Sikandra Rao will make considerable
enhancement in their agricultural productivity in 2030-31 too. This positive proliferation inagricultural productivity is due to agricultural development-induced outcomes in the study area
(Islam, 2020; Islam, Ahmad and Bano, 2020).
Figure 11: Future Trends of Agricultural Productivity for 2030-31

Yang's CYI

Hathras District
Future Trends of Agricultural Productivity for 2015-2030
115
110
105
100
95
90
85
80
Sasni

Hathras

2000-01

MursanSadabadBlocks

2014-15

SahapaoSikandra Rao

Hasayan2030-31 (Projection)

Conclusion
The aforesaid study of spatio-temporal differentiation of agricultural productivity denotes that allseven blocks namely, Sasni, Hathras, Mursan, Sadabad, Sahapao, Hasayan and Sikandra Rao
have made reasonable improvement in their overall indices values during the period from 200001 to 2014-15. Though the number of blocks under high productive regions of cereal crops has
been decreased but the index value is remaining high. The productivity regions of pulses showthat four blocks namely Hathras, Sadabad, Sikandra Rao and Hasayan have accelerated their
10

productivity throughout the analysis. The cash crops productivity regions reveal that it has madeconsiderable progress in both areas as well as production during the period from 2000-01 to
2014-15. It is found that Sahapao block has increased the productivity of cash crops from anindices value of 99.50 in 2000-01 to 114.62 in 2014-15, Hasayan block claimed from 98.67 to
106.02, Hathras block from 101.32 to 106.10, Sasni block from 95.64 to 97.01 and Sadabad
block grown from 101.85 to 102.49. The cultivable land is quite fixed in the study area, still, it
has marked a significant positive agricultural productivity during the period from 2000-01 to
2014-15. Similarly, the future trends of agricultural productivity for the year 2030-31 indicate thatthe study area has continuously boosted its productivity since 2000-01. This positive agricultural
performance perhaps is due to agricultural development-induced outcomes in the study area.
Moreover, the study area has a lacking of accessibility, invention and equal delivery mechanismof government-sponsored schemes, programs, policies and projects like irrigation, capacity
building programs, farmer sensitization issues and others (Islam, 2020). However, farmers must
be properly educated and trained through extension services that taught them how to use
modern agricultural inputs for increasing productivity.

Acknowledgments
The scholar (Dr. Pasarul Islam) is the awardee of ICSSR Post-Doctoral Fellowship. This paper
is largely an outcome of the Post-Doctoral Fellowship sponsored by the Indian Council ofSocial Science Research (ICSSR), New Delhi. However, the responsibility for the facts stated,
opinions expressed, and the conclusions drawn is entirely of the author.

References
1. Agriculture Census (2015) Department of Agriculture & Farmers Welfare. Available at:https://agcensus.nic.in/ (Accessed: 27 September 2022).
2. Ahmad, M. (2002) Agricultural Development in Uttarakhand. Aligarh Muslim University.
3. Ahmad, M. and Islam, P. (2017) ‘Agricultural Modernization and Rural Development: A Case
Study of Mahamaya Nagar District, Uttar Pradesh’, National Geographical Journal of India,
63(2), pp. 17–28.
4. Aktar, N. (2016) ‘Agricultural productivity and productivity regions in West Bengal’, The
NEHU Journal, XII(2), pp. 49–61.
5. Anríquez, G. and Stamoulis, K. (2007) ‘Rural Development and Poverty Reduction: isAgriculture still the key?’, ESA Working Paper/ Agricultural and Development Economics
Division, FAO, 7, pp. 1–38.
6. Chandramouli, C. and General, R. (2011) Census of India, censusindia.gov.in. New Delhi.
7. District Census Handbook (2011) Directorate of Census Operations UP, Lucknow.
8. District Economics and Statistics Officer and Development Block Level Offices, Department
of Planning, Government of Uttar Pradesh (2001) ‘District Statistical Bulletin, Hathras’.
9. District Economics and Statistics Officer and Development Block Level Offices, Department
of Planning, Government of Uttar Pradesh (2015) ‘District Statistical Bulletin, Hathras’.
10. FAO (2017) ‘The future of food and agriculture--Trends and Challenges’, Annual Report.
11. FAO (2020) ‘ World Food and Agriculture—Statistical Year Book 2020’, World Food andAgriculture-Statistical Yearbook.
12. Fischer, G. (2009) ‘World food and agriculture to 2030/50’, in Technical paper from theExpert Meeting on How to Feed the World, pp. 24–26.
13. Islam, P. (2020) Spatio temporal analysis of Agricultural modernization and regional
development in Hathras District of Uttar Pradesh. Aligarh Muslim University. Available at:https://shodhganga.inflibnet.ac.in/handle/10603/355828 (Accessed: 25 April 2022).
14. Islam, P., Ahmad, M. and Bano, E. (2020) ‘Exploring the Adoption Behaviour andDeterminants of Agricultural Modernization using Path Analysis in Hathras district, Uttar
Pradesh’, Annals of Rajasthan Geographical Association, XXXVI, pp. 1–12.
15. Kumar, A. and Sharma, P. (2022) ‘Impact of climate variation on agricultural productivity
andfood
securityinrural
India’.
Availableat:11https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4144089 (Accessed: 3 October2022).
16. Liu, J. et al. (2020) ‘Agricultural productivity growth and its determinants in south andsoutheast Asian countries’, Sustainability, 12(12), p. 4981. Available at:https://doi.org/10.3390/su12124981.
17. Munir, A. (1992) Agricultural Productivity and Regional Development. New Delhi, India:
Manak Publications Pvt Ltd.
18. Nattagh, N. (1986) Agriculture and Regional Development in Iran. Outwell, UK: Middle Eastand North African Studies press.
19. Nossal, K. and Gooday, P. (2009) ‘Raising Productivity Growth in Australian Agriculture’,
Australian Bureau of Agricultural Resource Economics [Preprint].
20. OECD (2008) Organisation for Economic Co-Operation and Development (OECD) Annual
Report 2008. Available at: https://www.oecd.org/newsroom/40556222.pdf (Accessed: 2
October 2022).
21. Pandit, S.N. (1983) Critical Study of Agricultural Productivity in Uttar Pradesh, 1951-1975.
New Delhi: Concept Publishing Company.
22. Rahaman, H. (2020) Diversified Cropping Pattern and Agricultural Development: A Case
Study from Malda District, India. Springer Nature.
23. Rehman, H. (2003) ‘Spatial Distribution of Agricultural Productivity and Its Correlates inNorth Bihar Plain’, The Geographer, 50(1), pp. 73–84.
24. Saxon, E. (1965) ‘ Concept of Productivity’, Agricultural Productivity, 6, pp. 112–120.
25. Shafi, M. (1984) Agricultural Productivity and Regional Imbalances, Annals of the
Association of American Geographers. New Delhi: Concept Publishing Company. Available
at: https://doi.org/10.1111/J.1467-8306.1984.TB01437.X.
26. Sharma, D. (2022) Indian Innovation, Not Jugaad-100 Ideas that Transformed India. Roli
Books Private Limited.
27. Sharma, S. and Sharma, M.L. (1993) ‘Planning optimum Agricultural Infrastructure for
Regional Development- A Case Study of Tarabfanj Tahsil’, pp. 33–46.
28. Siddiqui, S., Rehman, H. and Siddiqui, M. (1984) ‘Regional Analysis of Agricultural
Productivity in Bihar’, The Geographer, 31(1), pp. 77–85.
29. Socio-Economic Survey 2021-22 (no date). New Delhi.
30. Stamp, L.D. (1958) ‘The Measurement of Land Resources’, Geographical Review, 48(1), p.
1. Available at: https://doi.org/10.2307/211698.
31. Usmani, T.M. (1994) Impact of Agricultural Productivity on the level of Regional
Development in Bihar. Aligarh Muslim University.
32. Yang, W. (1965) ‘Methods of farm management investigations for improving farm
productivity, ’, FAO Agriculture Series.

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