Hostname: page-component-848d4c4894-5nwft Total loading time: 0 Render date: 2024-05-13T16:25:13.660Z Has data issue: false hasContentIssue false
  • English
  • Français

Prediction of spring maize yields using leaf color chart, chlorophyll meter, and GreenSeeker optical sensor

Published online by Cambridge University Press:  12 March 2021

Jagdeep-Singh Open the ORCID record for Jagdeep-Singh [Opens in a new window]  and
Varinderpal-Singh
Jagdeep-Singh*
Affiliation:
Department of Soil Science, Punjab Agricultural University, Ludhiana141 004, Punjab, India
Varinderpal-Singh
Affiliation:
Department of Soil Science, Punjab Agricultural University, Ludhiana141 004, Punjab, India
*
*Corresponding author. E-mail: jagdeep_76@pau.edu
Get access Rights & Permissions [Opens in a new window]

Summary

Predicting in-season crop yield is a unique tool for drawing important crop management decisions for precision farming. Field experiments were conducted at two locations in northwestern India under different agro-climatic zones to predict and validate spring maize yield using various in-season spectral indices. The spectral properties measured with leaf color chart (LCC), chlorophyll meter (SPAD meter), and GreenSeeker optical sensor were used to predict grain yield. A power function based on the Normalized Difference Vegetative Index (NDVI) measured with GreenSeeker optical sensor at V9 growth stage (9th leaf with fully exposed collar) presented higher values of coefficient of determination and explained 61% of the variability in spring maize grain yield, whereas NDVI measured at early and late growth stages were not reliable for the purpose. The spectral properties recorded with the SPAD meter and LCC rendered better grain yield estimates at VT growth stage (tasseling) and were respectively able to explain 75 and 76% variability in grain yield. The developed models were validated on an independent data set from another field experiment on spring maize. The normalized root mean square error (NRMSE) was <10% for LCC and SPAD at all the growth stages and at V9 growth stage for NDVI. The LCC, SPAD, and NDVI values adjusted with cumulative growing degree day were not helpful to improve NRMSE.

Keywords

Spring maizeYield predictionLeaf color chartChlorophyll meterOptical sensor
Type
Research Article
Information
Experimental Agriculture , Volume 57 , Issue 1 , February 2021 , pp. 45 - 56
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alam, M.M., Ladha, J.K., Foyjunnessa, R.Z., Khan, S.R., Harun, R., Khan, A.H. and Buresh, R.J. (2006). Nutrient management for increased productivity of rice-wheat cropping system in Bangladesh. Field Crops Research 96(2–3), 374–386.CrossRefGoogle Scholar
Ali, A.M., Thind, H.S., Sharma, S., and Singh, V. (2014). Prediction of dry direct-seeded rice yields using chlorophyll meter, leaf color chart, and GreenSeeker optical sensor in northwestern India. Field Crops Research 161, 11–15.CrossRefGoogle Scholar
Bijay-Singh, , Sharma, R.K., Jaspreet-Kaur, , Mangi, L.J., Kent, L.M., Yadvinder-Singh, , Varinderpal-Singh, , Parvesh, C., Choudhary, O.P., Rajeev, K.G., Thind, H.S., Jagmohan-Singh, , Uppal, H.S., Harmandeep, S.K., Ajay-Kumar, , Rajneet, K.U., Monika, V., William, R.R. and Gupta, R. (2011). Assessment of the nitrogen management strategy using an optical sensor for irrigated wheat. Agronomy for Sustainable Development 31, 589–603.CrossRefGoogle Scholar
Bijay-Singh, , Varinderpal-Singh, , Jaspreet-Kaur, , Sharma, R.K., Jat, M.L., Yadvinder-Singh, , Thind, H.S., Gupta, R.K., Chaudhary, O.P., Chandna, P., Khurana, H.S., Kumar, A. Jagmohan-Singh, , Uppal, H.S., Uppal, R.K., Monika, V. and Gupta, R. (2015). Site-specific fertilizer nitrogen management in irrigated transplanted rice (Oryza sativa) using an optical sensor. Precision Agriculture 16(4), 455–475.CrossRefGoogle Scholar
Blackmer, T.M. and Schepers, J.S. (1995). Use of chlorophyll meter to monitor nitrogen status and schedule fertigation for corn. Journal of Production Agriculture 8(1), 56–60.CrossRefGoogle Scholar
Bullock, D.G. and Anderson, D.S. (1998). Evaluation of the Minolta SPAD-502 chlorophyll meter for nitrogen management in corn. Journal of Plant Nutrition 21, 741–755.CrossRefGoogle Scholar
Dahnke, W.C., Swenson, L.J., Goos, R.J. and Leholm, A.G. (1988). In Choosing a crop yield goal Fargo, North Dakota: North Dakota State Extension Service.Google Scholar
Fageria, N.K., Baligar, V.C. and Li, Y.C. (2008). The role of nutrient efficient plants in improving crop yields in the twenty-first century. Journal of Plant Nutrition 31, 1121–1157.CrossRefGoogle Scholar
Freeman, K.W., Arnall, D.B., Mullen, R.W., Girma, K., Martin, K.L., Teal, R.K. and Raun, W.R. (2007). By-plant prediction of corn forage biomass and nitrogen uptake at various stages using remote sensing and plant height measures. Agronomy Journal 99, 530–536.CrossRefGoogle Scholar
Harrell, D.L., Tubana, B.S., Walker, T.S. and Phillips, S.B. (2011). Estimating rice grain yield potential using normalized difference vegetation index. Agronomy Journal 103, 1717–1723.CrossRefGoogle Scholar
Li, Y., He, L. and Zu, Y. (2010). Intraspecific variation in sensitivity to ultraviolet-B radiation in endogenous hormones and photosynthetic characteristics of 10 wheat cultivars grown under field conditions. South African Journal of Botany 76, 493–498.CrossRefGoogle Scholar
Loague, K. and Green, R.E. (1991). Statistical and graphical methods for evaluating solute transport models: overview and application. Journal of Contaminant Hydrology 7(1–2), 51–73.CrossRefGoogle Scholar
Martin, K.L., Girma, K., Freeman, K.W., Teal, R.K., Tubana, B., Arnall, D.B., Chung, B., Walsh, O., Solie, J.B., Stone, M.L. and Raun, W.R. (2007). Expression of variability in corn as influenced by growth stage using optical sensor measurements. Agronomy Journal 99, 384–89.CrossRefGoogle Scholar
Martin, K., Raun, W. and Solie, J. (2012). By-plant prediction of corn grain yield using optical sensor readings and measured plant height. Journal of Plant Nutrition 35(9), 1429–1439.CrossRefGoogle Scholar
Miao, Y., Mulla, D.J., Robert, P.C. and Hernandez, J.A. (2006). Within-field variation in corn yield and grain quality responses to nitrogen fertilization and hybrid selection. Agronomy Journal 98(1), 129–140.CrossRefGoogle Scholar
Mourtzinis, S., Arriaga, F.J., Balkcom, K.S. and Ortiz, B.V. (2013). Corn grain and stover yield prediction at the r1 growth stage. Agronomy Journal 105(4), 1045–1050.CrossRefGoogle Scholar
PAU (2017) Package of Practices for Crops of Punjab. Ludhiana, India: Punjab Agricultural University.Google Scholar
Payero, J. (2017). Introduction to Growing Degree Days. South Carolina: Agronomy Crops Edisto Research & Education Center. Clemson Cooperative Extension.Google Scholar
Piekielek, W.P., Fox, R.H., Toth, J.D. and Macneal, K.E. (1995). Use of a chlorophyll meter at the early dent stage of corn to evaluate nitrogen sufficiency. Agronomy Journal 87, 403–408.CrossRefGoogle Scholar
Raun, W.R., Johnson, G.V., Stone, M.L., Sollie, J.B., Lukina, E.V., Thomason, W.E. and Schepers, J.S. (2001). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal 93, 131–138.CrossRefGoogle Scholar
Raun, W.R., Solie, J.B., Johnson, G.V., Stone, M.L., Mullen, R.W., Freeman, K.W., Thomason, W.E. and Lukina, E.V. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agronomy Journal 94(4), 815–820.CrossRefGoogle Scholar
Raun, W.R., Solie, J.B., Stone, M.L., Martin, K.L., Freeman, K.W., Mullen, R.W., Zhang, H., Schepers, J.S. and Johnson, G.V. (2005). Optical sensor based algorithm for crop nitrogen fertilization. Communications in Soil Science and Plant Analysis 36(19–20), 2759–2781.CrossRefGoogle Scholar
Ritchie, J. and NeSmith, D. (1991). Temperature and crop development. Modeling Plant and Soil Systems 31, 5–29.Google Scholar
Sharma, L., Bu, H., Denton, A. and Franzen, D. (2015). Active-optical sensors using red NDVI compared to red edge NDVI for prediction of corn grain yield in North Dakota, U.S.A. Sensors 15(11), 27832–27853.CrossRefGoogle ScholarPubMed
Shaver, T., Khosla, R. and Westfall, D. (2014). Evaluation of two crop canopy sensors for nitrogen recommendations in irrigated maize. Journal of Plant Nutrition 37, 406–419.CrossRefGoogle Scholar
Shukla, A.K., Ladha, J.K., Singh, V.K., Dwivedi, B.S., Balasubramanian, V., Gupta, R.K., Sharma, S.K., Singh, Y., Pathak, H., Pandey, P.S., Padre, A.T. and Yadav, R.L. (2004). Calibrating the leaf color chart for nitrogen management in different genotypes of rice and wheat in a systems perspective. Agronomy Journal 96, 1606–1621.CrossRefGoogle Scholar
Singh, S.K., Kakani, V.G., Brand, D., Baldwin, B. and Reddy, K.R. (2008). Assessment of cold and heat tolerance of winter-grown canola (Brassica napus L.) cultivars by pollen-based parameters. Journal of Agronomy and Crop Science 194, 225–236.CrossRefGoogle Scholar
Singh, J., Singh, V. and Kaur, S. (2020). Precision nitrogen management improves grain yield, nitrogen use efficiency and reduces nitrous oxide emission from soil in spring maize. Journal of Plant Nutrition 43, 2311–2321.CrossRefGoogle Scholar
Solie, J.B., Raun, W.R., Whitney, R.W., Stone, M.L. and Ringer, J.D. (1996). Optical sensor based field element size and sensing strategy for nitrogen application. Transactions of the ASAE 39(6), 1983–1992.CrossRefGoogle Scholar
Stinner, R., Gutierrez, A. and Butler, G. (1974). An algorithm for temperature-dependent growth rate simulation. The Canadian Entomologist 106, 519–524.CrossRefGoogle Scholar
Stone, M.L., Solie, J.B., Raun, W.R., Whitney, R.W., Taylor, S.L. and Ringer, J.D. (1996). Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat. Transactions of the ASAE 39, 1623–1631.CrossRefGoogle Scholar
Teal, R.K., Tubana, B., Girma, K., Freeman, K.W., Arnall, D.B., Walsh, O. and Raun, W.R. (2006). In-season prediction of corn grain yield potential using normalized difference vegetation index. Agronomy Journal 98(6), 1488–1494.CrossRefGoogle Scholar
Tollenaar, M. (1989). Response of dry matter accumulation in maize to temperature: I. Dry matter partitioning. Crop Science 29, 1239–1246.CrossRefGoogle Scholar
Varinderpal-Singh, , Yadvinder-Singh, , Bijay-Singh, , Thind, H.S., Kumar, A. and Vashistha, M. (2011). Calibrating the leaf colour chart for need-based fertilizer nitrogen management in different maize (Zea mays L.) genotypes. Field Crops Research 120, 276–282.CrossRefGoogle Scholar
Witt, C., Pasuquin, J.M.C.A., Mutters, R. and Buresh, R.J. (2005). New leaf color chart for effective nitrogen management in rice. Better Crops 89, 6–39.Google Scholar
Zebarth, B.J. and Rosen, C.J. (2007). Research perspective on nitrogen BMP development for potato. American Journal of Potato Research 84, 3–18.CrossRefGoogle Scholar