Spatio-temporal variation and relationship between agricultural efficiency and irrigation intensity in a semi-arid region of India

doi:10.1016/j.regsus.2024.100144

Abstract

Abstract:

A surging population in Karnataka State, a semi-arid region in India, poses a threat to both food security and livelihood sustainability, necessitating a concentrated effort to bolster agricultural efficiency and achieve United Naton’s Sustainable Development Goal 2 (zero hunger). Therefore, in order to address the pressing issue of food scarcity in Karnataka, this study meticulously examined the spatio-temporal variation of agricultural efficiency and irrigation intensity in Karnataka, uncovering its significant dependence of agricultural efficiency on irrigation intensity. Specifically, this study used a one-way analysis of variance (ANOVA) to ascertain significant differences in the means of agricultural efficiency and irrigation intensity during 2004-2005 and 2018-2019. This study showed that the maximum improvement in agricultural efficiency index was recorded in Belgaum (40.24), Gulbarga (24.77), and Yadgir districts (22.92) between 2004-2005 and 2018-2019, which indicated the progressing trend and better scope for agriculture extension. On the contrary, some districts expressed threat (a decline of above 20.00 of agricultural efficiency index) and needed special care for the improvement of agricultural efficiency in four northern districts (Bagalkot, Bidar, Raichur, and Bijapur), three southern districts (Chitradurga, Chikballapur and Hassan), and two southern districts (Koppal and Gadag) in Karnataka. During 2004-2005, irrigation intensity varied from 3.19% to 56.39%, with the lowest irrigation intensity in Kodagu District and the highest irrigation intensity in Shimoga District. During 2018-2019, irrigation intensity changed from 0.77% to 72.77%, with the lowest irrigation intensity in Kodagu District and the highest in Dakshin Kannad District. Moreover, the research scrutinized the complex relationship between agricultural efficiency and irrigation intensity, with the correlation coefficient increased from 0.162 during 2004-2005 to 0.255 during 2018-2019. It implies that in both periods, a low positive correlation existed between these two variables. Over time, several factors (high-yield seeds and chemical fertilizers) other than irrigation intensity gradually became essential for agricultural efficiency. This research offers a wealth of valuable insights for regional planners and policy-makers contending with comparable challenges in various regions of India and other developing countries.

Key words: Agricultural efficiency, Irrigation intensity, One-way analysis of variance(ANOVA), Food security, Karnataka

Shibu DAS, Kaushal Kumar SHARMA, Suranjan MAJUMDER, Debabrata DAS, Indrajit Roy CHOWDHURY. Spatio-temporal variation and relationship between agricultural efficiency and irrigation intensity in a semi-arid region of India[J]. Regional Sustainability, 2024, 5(2): 100144.

Figures/Tables 16

Fig. 1.

Table 1

Table 2

Agricultural efficiency index, irrigation intensity, and residual value in Karnataka during 2004-2005 and 2018-2019."

District	Agricultural efficiency index					Irrigation intensity (%)					Residual value
District	2004-2005	Rank	2018-2019	Rank	Difference value	2004-2005	Rank	2018-2019	Rank	Difference value	2004-2005	2018-2019
Bagalkot	211.95	1	100.77	14	-111.18	45.31	4	62.50	4	17.19	93.29	-7.28
Belgaum	95.01	20	135.25	3	40.24	44.38	5	55.96	5	11.57	-22.41	29.59
Bellary	108.94	13	121.47	6	12.54	35.11	7	45.40	9	10.30	-8.28	19.67
Bengaluru Urban	155.16	2	126.35	5	-28.81	18.70	20	38.21	15	19.50	45.60	27.17
Bidar	138.00	5	79.06	25	-58.94	10.86	26	15.95	28	5.09	32.28	-12.00
Bijapur	128.29	6	68.88	29	-59.41	23.61	14	38.74	14	15.13	17.67	-30.50
Chamarajanagar	104.85	14	117.25	7	12.40	28.61	12	43.02	11	14.41	-7.02	16.32
Kolar	81.08	25	79.88	24	-1.20	21.96	17	25.45	25	3.49	-30.68	-14.65
Chikballapur^#	-	-	76.33	26	-28.52	-	-	30.14	20	1.54	-	-19.91
Chikmagalur	122.12	9	104.45	11	-17.67	11.21	25	27.20	22	15.99	15.50	9.29
Chitradurga	101.83	17	80.25	23	-21.58	18.35	21	31.05	18	12.70	-7.19	-16.32
Dakshin Kannad	95.56	18	99.78	16	4.21	55.17	2	72.77	1	17.61	-25.05	-12.02
Davangere	123.40	7	103.54	13	-19.86	34.67	8	51.48	6	16.82	5.10	-0.49
Dharwad	73.69	27	69.51	27	-4.18	12.11	24	16.90	27	4.79	-32.39	-21.89
Gadag	93.32	23	42.30	30	-51.02	21.31	18	22.45	26	1.14	-14.54	-51.13
Hassan	115.32	11	82.16	20	-33.16	23.81	13	26.60	23	2.79	4.27	-12.78
Haveri	93.67	22	81.15	22	-12.52	16.65	22	29.97	21	13.33	-14.46	-15.02
Kodagu	88.46	24	98.58	17	10.13	3.19	27	0.77	30	-2.43	-14.86	13.07
Koppal	102.43	15	68.96	28	-33.47	32.58	9	35.37	17	2.79	-12.61	-29.18
Mandya	122.32	8	116.40	8	-5.92	51.89	3	64.77	3	12.88	-0.56	7.52
Mysore	109.34	12	104.02	12	-5.32	35.45	6	40.57	12	5.12	-4.12	3.97
Raichur	142.97	3	111.34	10	-31.63	29.26	11	49.21	7	19.95	29.09	8.14
Bangalore Rural	142.20	4	140.41	2	-1.80	22.17	16	26.59	24	4.43	30.98	45.47
Ramanagara^##	-	-	130.84	4	-12.12	-	-	30.75	19	1.48	-	34.39
Shimoga	102.14	16	114.61	9	12.46	56.39	1	68.89	2	12.50	-23.46	4.23
Tumkur	94.29	21	81.81	21	-12.48	22.77	15	35.38	16	12.61	-16.99	-16.33
Udupi	95.04	19	100.18	15	5.14	32.26	10	47.11	8	14.84	-17.64	-2.25
Uttar Kannad	75.09	26	88.94	19	13.85	20.71	19	38.85	13	18.13	-34.65	-10.48
Gulbarga	121.64	10	146.41	1	24.77	14.55	23	11.30	29	-3.25	13.42	57.06
Yadgir^###	-	-	98.00	18	22.92	-	-	45.04	10	24.33	-	-3.67

Table 2

Fig. 2.

Fig. 3.

Fig. 4.

Table 3

Table 4

Table 5

Fig. 5.

Fig. 6.

Table S1

Table S2

Table S3

Table S4

Table S5

References 56

[1]	Alzarliani W.D., Arsyad M., Salam M., et al., 2019. Effect of App utilization on the agricultural efficiency of rural communities. IOP Conference Series: Earth and Environmental Science. 235(1), 012101, doi: 10.1088/1755-1315/235/1/012101.
[2]	Arnade C., 1998. Using a programming approach to measure international agricultural efficiency and productivity. J. Agric. Econ. 49(1), 67-84.
[3]	Bhatia S.S., 1967a. A new measure of agricultural efficiency in Uttar Pradesh, India. Econ. Geogr. 43(3), 244-260.
[4]	Bhatia S.S., 1967b. Spatial variarions, changes and trends in agricultural efficiency in uttar pradesh, 1953-1963. Indian Journal of Agricultural Economics. 22(1), 66-80.
[5]	Brahmanand P.S., Kumar A., Ghosh S., et al., 2013. Challenges to food security in India. Curr. Sci. 104(7), 841-846.
[6]	Bren D’Amour C., Reitsma F., Baiocchi, et al., 2017. Future urban land expansion and implications for global croplands. Proc. Natl. Acad. Sci. U. S. A. 114(34), 8939-8944.
[7]	Burja C., Burja V., 2016. Farms size and efficiency of the production factors in Romanian agriculture. Economics of Agriculture. 151(15), 361-374.
[8]	Chennareddy V., 1967. Production efficiency in south Indian agriculture. Journal of Farm Economics. 49(4), 816, doi: 10.2307/1236938.
[9]	Cupak A., Ziernicka-wojtaszek A., Krużel J., et. al., 2019. The use of hierarchical cluster analysis for grouping atmospheric precipitation in Poland. E3S Web of Conferences. 86, 00018, doi: 10.1051/e3sconf/20198600018.
[10]	Dayal E., 1984. Agricultural productivity in India: A spatial analysis. Ann. Assoc. Am. Geogr. 74(1), 98-123.
[11]	Department of Economics and Statistics, 2005. Statistical Abstract of Karnataka, 2004-2005. Bangalore: Directorate of Economics and Statistics Planning Programme Monitoring & Statistics Department.
[12]	Department of Economics and Statistics, 2019. Statistical Abstract of Karnataka, 2018-2019. Bangalore: Directorate of Economics and Statistics Planning Programme Monitoring & Statistics Department.
[13]	Dutta S., 2012. Assessment of agricultural efficiency and productivity: A study of Hugli district, West Bengal, India. International Journal of Current Research. 4(11), 190-195.
[14]	Gallup J.L., Radelet S., Warner A., 1997. Economic Growth and the Income of the Poor 1. [2023-04-05]. https://www.semanticscholar.org/paper/Economic-Growth-and-the-Income-of-the-Poor-1-Gallup-Radelet/799ffa0a344e221f6716de130b9c5804a24224ac.
[15]	Government of India Ministry of Agriculture Department of Agriculture & Cooperation Directorate of Economics & Statistics, 2014. Agricultural Statistics at a Glance 2014. New Delhi: Oxford University Press, 1-451.
[16]	Government of India Ministry of Agriculture Department of Agriculture & Cooperation Directorate of Economics & Statistics, 2017. Agricultural Statistics at a Glance 2017. New Delhi: Government of India Controller of Publications, 415-418.
[17]	Government of India Ministry of Agriculture Department of Agriculture & Cooperation Directorate of Economics & Statistics, 2019. Agricultural statistics at a glance 2019. New Delhi: Oxford University Press, 1-314.
[18]	Guo B.S., He D.W., Zhao X.D., et al., 2020. Analysis on the spatiotemporal patterns and driving mechanisms of China’s agricultural production efficiency from 2000 to 2015. Phys. Chem. Earth. 120, 102909, doi: 10.1016/j.pce.2020.102909.
[19]	Halder J.C., 2019. Modeling the effect of agricultural inputs on the spatial variation of agricultural efficiency in West Bengal, India. Model. Earth Syst. Environ. 5(3), 1103-1121.
[20]	Hesamian G., 2016. One-way ANOVA based on interval information. Int. J. Syst. Sci. 47(11), 2682-2690.
[21]	India Ministry of Home Affairs, 2011. Census of India. [2023-04-05]. https://censusindia.gov.in/census.website/.
[22]	Islam M.S., Siddiqui L., 2020. A geographical analysis of gender inequality in literacy among Muslims of West Bengal, India (2001-2011). GeoJournal. 85, 1325-1354.
[23]	Jayasree V., Venkatesh B., 2015. Analysis of rainfall in assessing the drought in semi-arid region of Karnataka State, India. Water Resour. Manage. 29, 5613-5630.
[24]	Jenkins J.C., Scanlan S.J., 2016. Food security in less developed countries, 1970 to 1990. Am. Sociol. Rev. 66(5), 718-744.
[25]	Kannan E., Balamurugan G., Narayanan S., 2021. Spatial economic analysis of agricultural land use changes: A case of peri-urban Bangalore, India. J. Asia. Pac. Econ. 26(1), 34-50.
[26]	Kendall M.G., 1953. The analysis of economic time-series-part I: Prices. J. R. Stat. Soc. Ser. D-Stat. 116(1), 11-34.
[27]	Khursheed V., Taufique M., 2019. Spatial analysis of horticulture efficiency and fruit production concentration in Kashmir Valley. GeoJournal. 85, 1635-1643.
[28]	Laha A., Kuri P.K., 2011. Measurement of allocative efficiency in agriculture and its determinants: Evidence from rural West Bengal, India. International Journal of Agricultural Research. 6(5), 377-388.
[29]	Li Z.J., Sarwar S., Jin T., 2021. Spatiotemporal evolution and improvement potential of agricultural eco-efficiency in Jiangsu Province. Front. Energy Res. 9, 746405, doi: 10.3389/fenrg.2021.746405.
[30]	Ma L., Long H.L., Tang L.S., et al., 2021. Analysis of the spatial variations of determinants of agricultural production efficiency in China. Comput. Electron. Agric. 180, 105890, doi: 10.1016/j.compag.2020.105890.
[31]	Majumdar J., Naraseeyappa S., Ankalaki S., 2017. Analysis of agriculture data using data mining techniques: application of big data. J. Big Data. 4(1), 20, doi: 10.1186/s40537-017-0077-4.
[32]	Majumder S., 2021. Spatial pattern of multi-dimensional regional disparities in the level of socio-economic development in West Bengal: A geographical analysis. Geosfera Indonesia. 6(3), 260-300.
[33]	Masiero S., Prakash A., 2015. The Politics of Anti-Poverty Artefacts:Lessons from the Computerization of the Food Security System in Karnataka. In: Proceedings of the Seventh International Conference on Information and Communication Technologies and Development. New York, Association for Computing Machinery, 1-10.
[34]	Misselhorn A., Aggarwal P., Ericksen P., et al., 2012. A vision for attaining food security. Curr. Opin. Environ. Sustain. 4(1), 7-17.
[35]	Mondal T.K., Sarkar S., 2021. Analysis of cropping intensity and irrigation intensity in North Twenty Four Parganas district, West Bengal, India. Geographica, Miscellanea Studies, Regional Development. 25(4), 1-13.
[36]	Murtagh F., Legendre P., 2014. Ward’ s hierarchical agglomerative clustering method: Which algorithms implement ward’ s criterion? J. Classif. 31, 274-295.
[37]	Nin-Pratt A., Yu B.X., Fan S.G., 2010. Comparisons of agricultural productivity growth in China and India. J. Prod. Anal. 33(3), 209-223.
[38]	Oehl F., Sieverding E., Ineichen K., et al., 2003. Impact of land use intensity on the species diversity of arbuscular mycorrhizal fungi in agroecosystems of central europe. Appl. Environ. Microbiol. 69(5), 2816-2824.
[39]	Ohlan R., 2013. Pattern of regional disparities in socio-economic development in India: District level analysis. Soc. Indic. Res. 114, 841-873.
[40]	Oshaug A., Haddad L., 2002. Nutrition: A Foundation for Development. Geneva: United Nations Administrative Committee on Coordination Nutrition/Standing Committee on Nutrition, 52.
[41]	Pang J.X., Chen X.P., Zhang Z.L., et al., 2016. Measuring eco-efficiency of agriculture in China. Sustainability. 8(4), 398, doi: 10.3390/su8040398.
[42]	Panth A.S., 1997. Social networks and food security in rural karnataka. Econ. Polit. Week. 32(15), 756-758.
[43]	Pattanayak U., Mallick M., 2018. A study on irrigation and agricultural productivity in odisha. Econ. Affa. 63(2), 317-323.
[44]	Pender J., Fafchamps M., 2006. Land lease markets and agricultural efficiency in Ethiopia. J. Afr. Econ. 15(2), 251-284.
[45]	Pimentel D., Huang X., Cordova A., et al., 1997. Impact of population growth on food supplies and environment. Popul. Env. 19(1), 9-14.
[46]	Rao V.M., Deshpande R.S., 2002. Food security in drought-prone areas: A study in karnataka. Econ. Polit. Week. 37(35), 3677-3681.
[47]	Rujasiri P., Chomtee B., 2009. Comparison of clustering techniques for cluster analysis. Int. J. Agric. Nat. Resour. 43(2), 378-388.
[48]	Sapre S.G., Deshpande V.D., 1964. Inter-district variations in agricultural efficiency in Maharashtra State. Indian Journal of Agricultural Economics. 19(1), 242-252.
[49]	Shafi M., 1960. Measurement of agricultural efficiency in uttar pradesh. Econ. Geogr. 36(4), 296-305.
[50]	Tang Z., Engel B.A., Pijanowski B.C., 2005. Forecasting land use change and its environmental impact at a watershed scale. J. Environ. Manage. 76(1), 35-45. pmid: 15854735
[51]	Tchale H., 2009. The efficiency of smallholder agriculture in Malawi. Afr. J. Agric. Resour. Econ.-AFJARE. 3(2), 101-121.
[52]	Toma E., Dobre C., Dona I., et al., 2015. DEA applicability in assessment of agriculture efficiency on areas with similar geographically patterns. Agriculture and Agricultural Science Procedia. 6, 704-711.
[53]	Wahid Z., Latiff A.I., Ahmad K., 2018. Application of one-way ANOVA in completely randomized experiments. Journal of Physics: Conference Series. 949(1), doi: 10.1088/1742-6596/949/1/012017.
[54]	Ward J.H., 1963. Hierarchical grouping to optimize an objective function. Journal of American Stastical Association. 58(301), 236-244.
[55]	Zeng W., Lu T., Liu Z.L., et al., 2021. Research on a laser ultrasonic visualization detection method for human skin tumors based on pearson correlation coefficient. Opt. Laser Technol. 141, 107117, doi: 10.1016/j.optlastec.2021.107117.
[56]	Zhou Z.X., Li M.T., 2017. Spatial-temporal change in urban agricultural land use efficiency from the perspective of agricultural multi-functionality: A case study of the Xi’an metropolitan zone. J. Geogr. Sci. 27(12), 1499-1520. doi: 10.1007/s11442-017-1449-6

Feature		Value	Date source
Total area		19.10×10⁶ hm²	Government of India Ministry of Agriculture Department of Agriculture & Cooperation Directorate of Economics & Statistics (2019)
Net crop area		10.70×10⁶ hm² (55.98% of the total reported area in Karnataka)
Total crop area		13.60×10⁶ hm²
Area sown more than once a year		2.89×10⁶ hm²
Net irrigated area		4.03×10⁶ hm²
Total irrigated area		4.74×10⁶ hm² (35.01% of the total crop area in Karnataka)
Cropping intensity		127.07%
Irrigation intensity		40.63%
Population	Total population	61.10×10⁶ persons	India Ministry of Home Affairs (2011)
	Male	31.00×10⁶ persons (50.69% of the total population in Karnataka )
	Female	3.01×10⁷ persons (49.31% of the total population in Karnataka)
Number of households		13.40×10⁶ households
Population density		3.19 persons/hm²
Literacy rate	Total literacy rate	75.36%
	Male literacy rate	82.47%
	Female literacy rate	68.08%
Occupation rate	Total occupation rate	45.60%
	Male occupation rate	59.00%
	Female occupation rate	31.90%
Agricultural workers		49.28% of the total workers in Karnataka
Total cultivators		23.61% of the total workers in Karnataka
Total agricultural laborers		25.67% of the total workers in Karnataka)
Agricultural land	Total land	7.83×10⁶ hm² (5.66% of the total land in India)	Government of India Ministry of Agriculture Department of Agriculture & Cooperation Directorate of Economics & Statistics (2014)
	Marginal land	3.85×10⁶ hm² (49.14% of the total land in Karnataka)
	Small land	2.14×10⁶ hm² (27.30% of the total land in Karnataka)
	Semi medium land	1.27×10⁶ hm² (16.17% of the total land in Karnataka)
	Medium land	0.51×10⁶ hm² (6.52% of the total land in Karnataka)
	Large land	6.76 hm² (0.86% of the total land in Karnataka)
Per capita land area		1.55 hm²/person

Cluster	Cluster criteria	Encompassing districts
Cluster	Cluster criteria	2004-2005	2018-2019
1	Medium to high agricultural efficiency and irrigation intensity (highly productive)	Bagalkot, Bangaluru Urban, Bidar, Bijapur, Chikmagalur, Hassan, Raichur, Bangalore rural, and Gulbarga districts	Bagalkot, Belgaum, Bellary, Bengaluru Urban, Chamarajanagar, Dakshin Kannad, Davangere, Mandya, Mysore, Raichur, Shimoga, Udupi, and Yadgir districts
2	Medium agricultural efficiency and irrigation intensity (moderately productive)	Belgaum, Bellary, Chamarajanagar, Dakshin kannad, Davanger, Koppal, Mandaya, Mysore, Shimoga, and Udupi districts	Bidar, Bijapur, Kolar, Chikballapur, Chikmagalur, Chitradurga, Dharwad, Gadag, Hassan, Haveri, Kodagu, Koppal, Tumkur, and Uttar Kannad districts
3	Low agricultural efficiency and medium irrigation intensity (low productive)	Kolar, Chitradurga, Dharwad, Gadag, Haveri, Kodagu, Tumkur, and Uttar Kannad districts	Bangalore Rural, Ramanagara, and Gulbarga districts

Variable	Group	Sum of squares	df	Mean square	F-statistic	P-value
Agricultural efficiency	Between groups	15.826	2.000	7.913	18.666	0.000
	Within groups	10.174	24.000	0.424	-	-
	Total	26.000	26.000	-	-	-
Irrigation intensity	Between groups	14.698	2.000	7.349	15.607	0.000
	Within groups	11.302	24.000	0.471	-	-
	Total	26.000	26.000	-	-	-

Variable	Group	Sum of squares	df	Mean square	F-statistic	P-value
Agricultural efficiency	Between groups	21.307	2.000	10.653	37.389	0.000
	Within groups	7.693	27.000	0.285	-	-
	Total	29.000	29.000	-	-	-
Irrigation intensity	Between groups	18.183	2.000	9.092	22.694	0.000
	Within groups	10.817	27.000	0.401	-	-
	Total	29.000	29.000	-	-	-

Variable	Method	F-statistic	df1	df2
Agriculture efficiency	Welch’s test	16.194	2.000	14.519
Agriculture efficiency	Brown-Forsythe test	18.686	2.000	12.042
Irrigation intensity	Welch’s test	16.537	2.000	15.726
Irrigation intensity	Brown-Forsythe test	16.298	2.000	22.301