Abstract
Micro-nutrient viz., zinc (Zn), copper (Cu), iron (Fe) and manganese (Mn) availability and their transformations have a direct relationship with soil fertility and ecosystem’s productivity. The wide-spread adoption of highly input-intensive rice-based cropping systems (RBCSs) has depleted micro-nutrients, especially in the light-textured soils of north-western India. We quantified micro-nutrients’ pool and investigated their transformations influencing their availability in soils under five different RBCSs, viz. rice-potato-mungbean, rice-peas-maize, rice-potato-maize, rice-wheat and rice-potato-melon and developed and evaluated the accuracy of artificial neural networks (ANNs) in estimating micro-nutrients’ availability in these soils. These results revealed a significant difference (p < 0.05) in micro-nutrients’ pool in soils under different RBCSs with a large variation in micro-nutrients pool, e.g., DTPA-Zn (1.9–3.6 mg kg−1), DTPA-Cu (41.4–47.0 mg kg−1), DTPA-Fe (6.2–7.8 mg kg−1) and DTPA-Mn (0.2–0.5 mg kg−1). A sequential speciation technique elucidated water-soluble + exchangeable fraction as the smallest (~0.1–1.2%), while the residual as the largest (~50%) fraction of the total micro-nutrients’ pool. The oxide-bound Zn, Cu, Fe, and Mn comprised ~44.2%, 42.7, 10.3, and 46.7%, respectively of the total micro-nutrients’ pool in soils under different RBCSs. Total-Zn was significantly higher (by ~33.3%) under rice-potato-maize than the rice-potato-mungbean, while total-Mn was higher (by ~22.8%) in rice-potato-mungbean than the rice-potato-melon system. Conversely, total-Fe content was significantly higher under rice-potato-melon, while total-Cu was significantly lower in rice-potato-melon as compared to the others. The ANN architecture (13-20-2-1 structure) comprised of 13 variables in input layers, 2 hidden layers with 20 and 2 neurons, respectively and micro-nutrient (Zn, Cu, Fe, and Mn) content as output layer with Levenberg-Marquardt algorithm was identified as the best ANN topology. This ANN architecture showed the highest accuracy of micro-nutrients prediction with a coefficient of determination (R2) value of 0.750–0.921**, root mean square error (RMSE) value of 0.057–0.954 mg kg−1 and mean absolute percentage error (MAPE) value of −0.944 and 1.451%. The ANN model architecture produced highly promising results in predicting the micro-nutrients availability in soil under RBCSs. The inclusion of short-duration legume as well as deep-rooted crops helps in enhancing micro-nutrient pool for increased crop production and improved soil health of light textured soils of north-western India.
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References
Ahmed T, Mahfuz M, Ireen S, Ahmed AS, Rahman S, Islam MM, Cravioto A (2012) Nutrition of children and women in Bangladesh: trends and directions for the future. J. Health Pop Nutr 31:1–12. https://doi.org/10.3329/jhpn.v30i1.11268
Atanassova I, Okazaki M (1997) Adsorption desorption characteristics of high levels of copper in soil clay fractions. Water Air Soil Pollut 98:213–228. https://doi.org/10.1023/A:1026407326109
Bailey RL, West KPJ, Black RE (2015) The epidemiology of global micronutrient deficiencies. Annal Nutr Metabol 66:22–33. https://doi.org/10.1159/000371618
Banjara TR, Bohra JS, Kumar S, Singh T, Shori A, Prajapat K (2022) Sustainable alternative crop rotations to the irrigated rice-wheat cropping system of Indo-gangetic plains of India. Arch Agron Soil Sci 68:1568–1585. https://doi.org/10.1080/03650340.2021.1912324
Batista FM, Migliolo TC (2006) Food and nutrition in northeastern Brazi. Instituto Materno Infantil Professor Fernando Figueira (IMIP). pp. 101-110
Behera SK, Singh D, Dwivedi BS (2009) Changes in fraction of iron, manganese, copper and zinc in soil under continuous cropping for more than three decades. Comm Soil Sci Plant Anal 40:1380–1407. https://doi.org/10.1080/00103620902818054
Bhatt R, Hussain A, Singh P (2019) Scientific interventions to improve land and water productivity for climate-smart agriculture in South-Asia. Chapter-24, In: Mirza H (ed) Agronomic Crops Volume-2: management Practices, pp 449–458
Bhatt R, Singh P, Majumder D, Kaur G (2023) Climate-smart technologies for reducing water footprints in different cropland ecosystems-a meso analysis. Proc Indian Nat Sci Acad. https://doi.org/10.1007/s43538-022-00148-4
Bhatt R, Kukal SS, Busari MA, Arora S, Yadav M (2016) Sustainability issues on rice–wheat cropping system. Int Soil Water Conserv Res 4:64–74. https://doi.org/10.1016/j.iswcr.2015.12.001
Bhatt R, Singh P, Hussain A, Timsina J (2021) Rice-wheat system in the north-west Indo-Gangetic Plains of South Asia: issues and technological interventions for increasing productivity and sustainability. Paddy Water Environ 19:345–365. https://doi.org/10.1007/s10333-021-00846-7
Bhatt R, Singh P, Kaur G (2022) Soil management vis-à-vis carbon sequestration in relation to land-use cover/change in terrestrial ecosystem-a review. In: Hanuzzaman M, Ahammed GJ, Nahar K (eds) Managing plant production under changing environment. Springer Nature, Singapore, pp 43–78. https://doi.org/10.1007/978-981-16-5059-8_3
Brar S, Dhaliwal S, Sharma V, Sharma S, Kaur M (2023) Soil quality assessment in diversified long-term experimentation under different agriculturally based cropping systems. J Soil Sci Plant Nutr. https://doi.org/10.1007/s42729-023-01134-5
Brunetto G, Miotto A, Ceretta CA, Schmitt DE, Heinzen J, de Moraes MP, Canton L, Tiecher TL, Comin JJ, Girotto E (2014) Mobility of copper and zinc fractions in fungicide-amended vineyard sandy soils. Arch Agron Soil Sci 60:609–624. https://doi.org/10.1080/03650340.2013.826348
Cakmak I (2009) Enrichment of fertilizers with zinc: an excellent investment for humanity and crop production in India. J Trace Elem Med Biol 29:281–289. https://doi.org/10.1016/j.jtemb.2009.05.002
Chen Y, Stevenson FJ (1986) Soil organic matter interaction with trace elements. In: Chen Y, Avnimelech Y (eds) The role of organic matter in modem agriculture. Mal1inus Nijhoff Publishers, Dordrecht, pp 73–116. https://doi.org/10.1007/978-94-009-4426-8_5
Cornell R, Schwertmann U (2003) The iron oxides. Structure, properties, reactions, occurrences and uses, 2nd edn completely revised and extended Edition. Wiley-VCH, p 664p. https://doi.org/10.1002/3527602097
Dasappagol A, Bellakki MA, Ravi MV, Kuchanur PH, Jat ML (2017) Distribution of zinc fractions in surface alfisol after five years of conservation agriculture practices in rainfed pigeonpea. Int J Chem Stud 5:227–232
Davidson EA, Chorover J, Dail DB (2003) A mechanism of abiotic immobilization of nitrate in forest ecosystems: the ferrous wheel hypothesis. Glob Chang Biol 9:228–236. https://doi.org/10.1046/j.1365-2486.2003.00592.x
de Paul OV, Lal R (2016) Towards a standard technique for soil quality assessment. Geoderma 265:96–102. https://doi.org/10.1016/j.geoderma.2015.11.023
Dhaliwal SS, Naresh RK, Walia MK, Gupta RK, Mandal A, Singh R (2019) Long-term effects of intensive rice–wheat and agroforestry based cropping systems on build-up of nutrients and budgets in alluvial soils of Punjab. Arch Agron Soil Sci 66:330–342. https://doi.org/10.1080/03650340.2019.1614564
Erzin Y, Rao BH, Singh DN (2008) Artificial neural network models for predicting soil thermal resistivity. Int J Therm Sci 47:1347–1358. https://doi.org/10.1016/j.ijthermalsci.2007.11.001
Fageria NK, Baligar VC, Clark RB (2002) Micronutrients in crop production. Adv Agron 77:185–268. https://doi.org/10.1016/S0065-2113(02)77015-6
Fernandes MMH, Anderson PC, Carolina F, Flavio M, da Silva CC, Marta D (2019) Estimation of soil organic matter content by modeling with artificial neural networks. Geoderma. 350:46–51. https://doi.org/10.1016/j.geoderma.2019.04.044
Ferreira SR, Gonzalez ESC, Albuquerque EC, Arruda IKG, Diniz ADS, Figueroa JS, Pereira APC (2011) Prevalence of anemia in under five-year-old children in a children’s hospital in Recife, Brazil. Rev Bras Hematol Hemoter. 33:100–104. https://doi.org/10.5581/1516-8484.20110028
Festa RA, Thiele DJ (2011) Copper: an essential metal in biology. Curr Biol 21:877–883. https://doi.org/10.1016/j.cub.2011.09.040
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. 1st ed. The MIT Press, Cambridge, United Kingdom. https://mitpress.mit.edu/9780262035613/deep-learning/
Han X, Li X, Uren N, Tang C (2011) Zinc fractions and availability to soybeans in representative soils of Northeast China. J Soils Sed 11:596–606. https://doi.org/10.1007/s11368-011-0336-5
Haque KS, Eberbach PL, Weston LA, Dyall-Smith M, Howitt JA (2016) Variable impact of rice (Oryza sativa) on soil metal reduction and availability of pore water Fe2+ and Mn2+ throughout the growth period. Chem Ecol 32:182–200. https://doi.org/10.1080/02757540.2015.1122000
Ito E, Ono K, Ito YM, Araki M (2008) A neural network approach to simple prediction of soil nitrification potential: a case study in Japanese temperate forests. Ecol Model 219:200–211. https://doi.org/10.1016/j.ecolmodel.2008.08.011
Jia HY, Chen J, Yu HL, Liu DY (2010) International conference on machine learning and cybernetics. Soil fertility grading with Bayesian network transfer learning, IEEE, pp 1159–1163. https://doi.org/10.1109/ICMLC.2010.5580915
Kaur G (2020a) Artificial neural networks to predict soil organic carbon distribution using physical and chemical properties of soils under different cropping systems in India. Tathapi 19:353–365
Kaur G (2020b) Detection of bacterial leaf blight disease of cotton (Gossypium hirsutum L.) using convolution neural network (CNN): simulations with support vector machine (SVM) and Naïve bayes algorithms. Design Engineering 2021:2384–2400
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436
Liu E, Pimpin L, Shulkin M, Kranz S, Duggan CP, Mozaffarian D, Fawzi WW (2018) Effect of zinc supplementation on growth outcomes in children under 5 years of age. Nutrients. 10:377. https://doi.org/10.3390/nu10030377
Lu F, Lee C, Wang C (2015) The influence of arbuscular mycorrhizal fungi inoculation on yam (Dioscorea spp.) tuber weights and secondary metabolite content. Peer J 3:12–66. https://doi.org/10.7717/peerj.1266
Marschner H (2012) Mineral nutrition of higher plants, 3rd edn. Academic Press, London, UK
Mathur SP, Levesque MP (1983) Effect of liming on the yield, nutrition and copper status of potatoes, carrots and onions grown sequentially in two peat soils. Can J Soil Sci 63:229–244. https://doi.org/10.4141/cjss83-024
McBride MB (1994) Environmental chemistry of soils. Oxford University Press, New York, p 416
McDaniel SH, Grus CL, Cubic BA, Hunter CL, Kearney LK, Schuman CC, Johnson SB (2014) Competencies for psychology practice in primary care. Am Psychol 69:409–429. https://doi.org/10.1037/a0036072
Meena BL, Kumar P, Kumar A, Meena RL, Kaledhonkar MJ, Sharma PC (2018) Zinc and iron nutrition to increase the productivity of pearl millet-mustard cropping system in salt affected soils. Int J Curr Microbiol App Sci 7:3201–3211
Mittal S, Saini SP, Singh P (2022) Manganese availability and transformations in soil profiles under different wheat based cropping systems in north-western India. Indian J Agri Sci 92:689–694. https://doi.org/10.56093/ijas.v92i6.101593
Mittal S, Saini SP, Meena HM, Singh P (2023) Soil manganese dynamics and performance of wheat cultivars in relation to soil and foliar manganese application with and without manures in North-western, India. J Soil Sci Plant Nutr. https://doi.org/10.1007/s42729-023-01151-4
Moharana PC, Naitam RK, Verma TP, Meena RL, Kumar S, Tailor BL, Singh RS, Singh SK, Samal SK (2017) Effect of long-term cropping systems on soil organic carbon pools and soil quality in western plain of hot arid India. Arch Agron Soil Sci 63:1661–1675. https://doi.org/10.1080/03650340.2017.1304637
Moosavi AA, Nematollahi MA, Rahimi M (2021) Predicting water sorptivity coefficient in calcareous soils using a wavelet–neural network hybrid modeling approach. Environ Earth Sci 80:226. https://doi.org/10.1007/s12665-021-09518-5
Moreno-Jim E, Plaza C, Saiz H, Manzano R, Flagmeier M, Maestre FT (2019) Aridity and reduced soil micronutrient availability in global drylands. Nat Sustainability 2:371–377. https://doi.org/10.1038/s41893-019-0262-x
Mouazen AM, Kuang BJ, De Baerdemaeker H, Ramon (2010) Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma 156: 23-31. https://doi.org/10.1016/j.geoderma.2010.03.001
Mucherino A, Papajorgji P, Pardalos PM (2009) A survey of data mining techniques applied to agriculture. Oper Res Int J 9:121–140. https://doi.org/10.1007/s12351-009-0054-6
Nayyar VK, Sadana US Takkar PN (1985) Methods and rates of application of Mn and its critical levels for wheat following rice on coarse textured soils. Fertl Res 8: 173-178.
Najafi E, Sharifi MA, Pourghassem H (2009) Estimation of oil flow rate in a well using artificial neural network. Petrol Sci Tech 27:1647–1658
Nawaz FM, Nadeem F, Siddique KHM, Lal R (2019) Rice–wheat cropping systems in South Asia: issues, options and opportunities. Crop. Crop Past Sci 70:395. https://doi.org/10.1071/cp18383
Norouzi M, Khoshgoftarmanesh H, Afyuni M (2014) Zinc fractions in soil and uptake by wheat as affected by different preceding crops. Soil Sci Plant Nutr 60:670–678. https://doi.org/10.1080/00380768.2014.937674
Ojha S, Sourabh S, Dasgupta S, Das DK, Sarkar A (2018) Influence of different organic amendments on Fe, Mn, Cu and Zn availability in Indian soils. Int J Curr Microbiol Appl Sci 7:2435–2445. https://doi.org/10.20546/ijcmas.2018.705.280
Papailiou I, Spyropoulos F, Trichakis I, Karatzas GP (2022) Artificial neural networks and multiple linear regression for filling in missing daily rainfall data. Water 14:2892. https://doi.org/10.3390/w14182892
Pawar PS, Khade KR, Bhite BR (2017) Effect of micronutrients on growth, yield and quality of sweet orange cv. Mosambi. Bioinfolet 14:202–204
Priyanka SSK, Meena RH (2017) Fractionation and distribution of zinc under integrated nutrient management system on maize-wheat cropping system in Typic Haplustepts. J Pharmac Phytochem 6:2301–2305
Puniya R, Pandey PC, Bisht PS, Singh DK, Singh AP (2019) Effect of long-term nutrient management practices on soil micronutrient concentrations and uptake under a rice–wheat cropping system. The J Agric Sci 157:226–234. https://doi.org/10.1017/S0021859619000509
Rajneesh S, Sankhyan RP, Kumar NKR, Kumar R (2017) Long-term effect of fertilizers and amendments on depth-wise distribution of available NPK micronutrient cations, productivity, and NPK uptake by maize-wheat system in an acid alfisol of northwestern Himalayas. Comm Soil Sci Plant Anal 48:2193–2209. https://doi.org/10.1080/00103624.2017.1408816
Randhawa M, Dhaliwal SS, Sharma V, Toor AS, Sharma S, Kaur M (2021) Ensuring yield sustainability and nutritional security through enriching manures with fertilizers under rice–wheat system in North-western India. J Plant Nutr. https://doi.org/10.1080/01904167.2021.1943748
Raza S, Aslam M, Haq MIU et al (2017) Micronutrient deficiencies in crops grown in South Asia: A review. Environ Sci Pollut Res 24:20629–20644
Ritchie GSP, Dolling PJ (1985) The role of organic matter in soil acidification. Soil Res 23:569–576. https://doi.org/10.1071/SR9850569
Rudani K, Patel V, Prajapati V (2018) The importance of zinc in plant growth-a review. Int Res J Nat Appl Sci 5:2349–4077
Sadana US, Manchanda JS, Khurana MPS, Dhaliwal SS, Singh H (2010) The current scenario and efficient management of zinc, iron, and manganese deficiencies. Better Crops South Asia, pp 24–26
Sarangi A, Bhattacharya AK (2005) Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India. Agric Water Manag 78:195–208. https://doi.org/10.1016/j.agwat.2005.02.001
Sarkar S, Chakraborty D, Kar S, Manna MC (2017) Micronutrient availability in soil and their uptake by rice in Indo-Gangetic Plains of India. Comm Soil Sci Plant Anal 48:1702–1714
Scarciglia F, Tuccimei P, Vacca A, Barca D, Pulice I, Salzano R, Soligo M (2011) Soil genesis, morphodynamic processes and chronological implications in two soil transects of SE Sardinia, Italy: traditional pedological study coupled with laser ablation ICP-MS and radionuclide analyses. Geoderma 162:39–64. https://doi.org/10.1016/j.geoderma.2011.01.004
Schmidt SB, Eisenhut M, Schneider A (2020) Chloroplast transition metal regulation for efficient photosynthesis. Trends Plant Sci 25:817–828. https://doi.org/10.1016/j.tplants.2020.03.003
Shambhavi S, Kumar R, Sharma SP, Verma G, Sharma RP, Sharma SK (2017) Long-term effect of inorganic fertilizers and amendments on productivity and root dynamics under maize-wheat intensive cropping in an acid Alfisol. J Appl Nat Sci 9:2004–2012
Sharma S, Chander G, Verma TS (2014) Copper dynamics in a Typic Hapludalf under rice-wheat cropping system after twelve years of annual lantana camara L. residue incorporation. J Plant Nutr 37:1093–1103. https://doi.org/10.1080/01904167.2014.888738
Sharma S, Kaur G, Singh P, Alamri S, Kumar R, Siddiqui MH (2022b) Nitrogen and potassium application effects on productivity, profitability and nutrient use efficiency of irrigated wheat (Triticum aestivum L.). PLoS ONE 17:e0264210. https://doi.org/10.1371/journal.pone.0264210
Sharma S, Singh P, Sodhi GPS (2020) Soil organic carbon and biological indicators of uncultivated vis-à-vis intensively cultivated soils under rice–wheat and cotton–wheat cropping systems in South-western Punjab. Carbon Manag 11:681–695. https://doi.org/10.1080/17583004.2020.1840891
Sharma S, Singh P, Angmo P, Dhaliwal SS (2021) Micro-nutrient pools and their mobility in relation to land-use in a cold high-altitude Himalayan mountaneous region. Agrofor Systems 95:1395–1412. https://doi.org/10.1007/s10457-021-00623-9
Sharma S, Singh P, Angmo P, Satpute S (2022a) Total and labile pools of organic carbon in relation to soil biological properties under contrasting land-use systems in a dry mountainous region. Carbon Manag. 13:352–371. https://doi.org/10.1080/17583004.2022.2089236
Sharma S, Singh P (2023) Tillage intensity and straw retention impacts on soil organic carbon, phosphorus and biological pools in soil aggregates under rice-wheat cropping system in Punjab, north-western India. Euro J Agron. 149:126913. https://doi.org/10.1016/j.eja.2023.126913
Sharma S, Singh P, Dhaliwal SS, Kaur G, Sodhi GPS (2023) Changes in micro-nutrients and their fractions in relation to soil quality indices under rice-wheat, cotton-wheat and agroforestry in north-western India. J Soil Sci Plant Nutr. https://doi.org/10.1007/s42729-023-01490-2
Sharma U, Kumar P (2016) Micronutrient research in India: extent of deficiency, crop responses and future challenges. Int J Adv Res 4:1402–1406. https://doi.org/10.21474/IJAR01/234
Sharma V, Negi SC, Rudra RP, Yang S (2003) Neural networks for predicting nitrate–nitrogen in drainage water. Agric Water Manag 63:169–183. https://doi.org/10.1016/S0378-3774(03)00159-8
Sheela PJ, Sivaranjani K, Phil M (2015) A brief survey of classification techniques applied to soil fertility prediction. Int Conf Eng Trends Sci Hum 80:3–12
Shukla AK, Tiwari PK, Prakash C (2014) Micronutrients deficiencies vis-a-vis food and nutritional security of India. Indian J Fertil 10:94–112
Silva DG, Franceschini SC, Priore SE, Ribeiro SM, Szarfarc SC, Souza SB, Almeida LP (2002) Iron deficiency anemia in children aged 6 to 12 months treated in the public health network in the city of Viçosa, Minas Gerais. Rev Nutr 15:301–308
Singh P, Benbi DK (2016) Effect of inorganic fertilizers and farm yard manure on physical properties of soil under rice-wheat cropping. Agric Res J (PAU) 53:328–333. https://doi.org/10.5958/2395-146X.2016.00064.8
Singh P, Benbi DK (2018) Soil organic carbon pool changes in relation to slope position and land-use in Indian lower Himalayas. Catena 166:171–180. https://doi.org/10.1016/j.catena.2018.04.006
Singh P, Benbi DK (2021a) Physical and chemical stabilization of soil organic matter in cropland ecosystems under rice-wheat, maize-wheat and cotton-wheat cropping systems in north-western India. Carbon Manage 12:603–621. https://doi.org/10.1080/17583004.2021.1992505
Singh P, Benbi DK (2021b) Physical and chemical stabilization of soil organic matter in cropland ecosystems under rice-wheat, maize-wheat and cotton-wheat cropping systems in north-western India. Carbon Manag 12:603–621. https://doi.org/10.1080/17583004.2021.1992505
Singh P, Benbi DK (2022) Nutrient management effects on carbon input through root and shoot biomass in a rice-wheat system. Agric Res J 59:135–145. https://doi.org/10.5958/2395-146X.2022.00021.7
Singh P, Singh H, Khurana MPS (2010) Micronutrient fertility status of recent floodplain soils of Punjab. Agric Res J (PAU) 46:140–144
Singh SK, Kumar M, Pandey CB, Ghosh A, Mukhopadhyay S, Sarkar D (2013) Differences in soil properties between irrigation and cropping sequences in the Thar Desert of India. Arid Land Res Manag 27:17–31. https://doi.org/10.1080/15324982.2012.719577
Singh H, Singh P, Singh D (2014) Chemical fractionation of heavy metals and nutrients in sludge and waste water generated by Coca-Cola soft drink industry. Arch Agron Soil Sci 61:119–138. https://doi.org/10.1080/03650340.2014.924106
Singh P, Singh G, Sodhi GPS (2020) Energy and carbon footprints of wheat establishment following different rice residue management strategies vis-à-vis conventional tillage coupled with rice residue burning in north-western India. Energy 200:117554. https://doi.org/10.1016/j.energy.2020.117554
Singh P, Benbi DK, Verma G (2021a) Nutrient management impacts on nutrient use efficiency and energy, carbon, and net ecosystem economic budget of rice-wheat cropping system in north-western India. J Soil Sci Plant Nutr 21:559–577. https://doi.org/10.1007/s42729-020-00383-y
Singh P, Singh G, Sodhi GPS, Benbi DK (2021b) Accounting carbon footprints and applying data envelopment analysis to optimize input induced greenhouse gases emissions under rice-wheat cropping system in north-western India. J Soil Sci Plant Nutr 21:3030–3050. https://doi.org/10.1007/s42729-021-00587-w
Singh P, Bijay-Singh, Farmaha BS (2023) Nutrient management impacts on organic carbon pool in soils under different cropping systems in the Indo-Gangetic Plains in South Asia. Proc Indian Nat Sci Acad. https://doi.org/10.1007/s43538-023-00192-8
Singh P, Singh G, Sodhi GPS (2019a) Energy auditing and optimization approach for improving energy efficiency of rice cultivation in south-western Punjab. Energy 174:169–179. https://doi.org/10.1016/j.energy.2019.02.169
Singh P, Singh G, Sodhi GPS (2019b) Applying DEA optimization approach for energy auditing in wheat cultivation under rice-wheat and cotton-wheat cropping systems in north-western India. Energy 181:18–28. https://doi.org/10.1016/j.energy.2019.05.147
Sipöcz N, Tobiesen FA, Assadi M (2011) The use of artificial neural network models for CO2 capture plants. Appl Energy 88:2368–2376. https://doi.org/10.1016/j.apenergy.2011.01.013
Soltani M, Mehnatkesh A, Taghizadeh-Mehrjardi R (2021) Prediction of copper, zinc and manganese availability in soil using artificial neural networks. Comm Soil Sci Plant Anal 52:64–76
Srisomkiew S, Kawahigashi M, Limtong P, Yuttum O (2022) Digital soil assessment of soil fertility for Thai jasmine rice in the Thung Kula Ronghai region. Thailand. Geoderma 409:115597. https://doi.org/10.1016/j.geoderma.2021.115597
Sun Y, Guo G, Shi H, Liu M, Keith A, Li H, Jones KC (2020) Decadal shifts in soil pH and organic matter differ between land uses in contrasting regions in China. Sci Total Environ 740:139904. https://doi.org/10.1016/j.scitotenv.2020.139904
Taki M, Rohani A, Soheili-Fard F, Abdeshahi A (2016) Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models. J Clean Prod 172:3028–3041. https://doi.org/10.1016/j.jclepro.2017.11.107
Tohidi M, Sadeghi M, Mousavi SR, Mireei SA (2012) Artificial neural network modeling of process and product indices in deep bed drying of rough rice. Turk J Agric Fores 36:738–748. https://doi.org/10.3906/tar-1106-44
Tripathi D, Singh K, Singh S, MishraS CDK, Dubey NK (2015) Micronutrients and their diverse role in agricultural crops: advances and future prospective. Acta Physiol Plant 37:139–146. https://doi.org/10.1007/s11738-015-1870-3
Trontelj ML, Chambers O (2021) Machine learning strategy for soil nutrients prediction using spectroscopic method. Sensors 21:4208. https://doi.org/10.3390/s21124208
Vafaei M, Ramezanian A, Zabihi M (2020) Predicting availability of zinc and copper in soil using artificial neural networks. Int J Environ Sci Tech 17:355–366
Venkatesh MS, Hazra KK, Ghosh PK, Khuswah BL, Ganeshamurthy AN, Ali M, Singh J, Mathur RS (2017) Long term effect of crop rotation and nutrient management on soil-plant nutrient cycling and nutrient budgeting in Indo-Gangetic plains of India. Arch Agron Soil Sci 63:1–16. https://doi.org/10.1080/03650340.2017.1320392
Viets FGJ (1962) Micronutrient availability, chemistry and availability of micronutrients in soils. J Agric Food Chem 10:174–178. https://doi.org/10.1021/jf60121a004
Walkley A, Black TA (1934) An examination of the Degtjaeff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci 37:29–38. https://doi.org/10.1097/00010694-193401000-00003
Welch RM, Graham RD, Cakmak I (2013) Linking agricultural production practices to improving human nutrition and health, expert paper written for ICN2 in Second International Conference on Nutrition Preparatory Technical Meeting,13-15 November, Rome
Yi Z, Yan C, Changquan W, Guo-Xiang X (2012) Characters and influencing factors of the distribution of cultivated available soil Fe, Mn, Cu and Zn in Xichang City, Sichuan Province, China. J Geogra Geol 4:115–123. https://doi.org/10.5539/jgg.v4n2p115
Zhao YC, Shi XZ, Yu DS, Zhao YF, Sun WX, Wang HJ (2005) Different methods for prediction of spatial patterns of soil organic carbon density in Hebei province, China. Acta Pedol Sinica 42:379–385
Zhao Z, Yang Q, Benoy G, Chow TL, Xing Z, Rees HW, Meng FR (2010) Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes. Can J Soil Sci 90:75–87. https://doi.org/10.4141/CJSS08057
Zhao Z, Chow TL, Rees HW, Yang Q, Xing Z, Fan-Rui M (2009) Predict soil texture distributions using an artificial neural network model. Comp Elect Agric 65:36–48. https://doi.org/10.1016/j.compag.2008.07.008
Zhu H, Ding H, Bi R, Hou M (2021) Large-extent spatial heterogeneity of soil bioavailable micronutrients and the relative roles of environmental indicators on them within maize fields. Ecol Indic 130:108071. https://doi.org/10.1016/j.ecolind.2021.108071129
Zhu H, Zhao Y, Nan F, Duan Y, Bi R (2016) Relative influence of soil chemistry and topography on soil available micronutrients by structural equation modeling. J Soil Sci Plant Nutr 16:1038–1051. https://doi.org/10.4067/S0718-95162016005000076
Zou P, Yang J, Fu J, Liu G, Li D (2010) Artificial neural network and time series models for predicting soil salt and water content. Agric Water Manag 97:2009–2019. https://doi.org/10.1016/j.agwat.2010.02.011
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Thanks are due to the Head, Department of Soil Science, Punjab Agricultural University, for providing the necessary laboratory and field facilities.
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Sharma, S., Kaur, G., Singh, P. et al. Development of Artificial Neural Networks for Predicting Soil Micro-Nutrients Availability Under Rice-Based Cropping Systems of North-western India. J Soil Sci Plant Nutr 24, 884–904 (2024). https://doi.org/10.1007/s42729-023-01593-w
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DOI: https://doi.org/10.1007/s42729-023-01593-w