Spatiotemporal response of maize yield to edaphic and meteorological conditions in a saline farmland

Agronomy Journal

Volumn 106, Issue 6, 2014, Pages 2163-2174

  Scudiero, Elia ^a,b   Teatini, Pietro ^b   Corwin, Dennis L ^a   Dal Ferro, Nicola ^b   Simonetti, Gianluca ^b   Morari, Francesco ^b  

^a U S SALINITY LABORATORY   (United States)

^b UNIVERSITY OF PADOVA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84908222973     PISSN: 00021962     EISSN: 14350645     Source Type: Journal    
DOI: 10.2134/agronj14.0102     Document Type: Article

References (47)

1. Abendroth, L.J., R.W. Elmore, M.J. Boyer, and S.K. Marlay. 2011. Corn growth and development. PMR 1009. Iowa State Univ. Ext., Ames.
2. Acevedo-Opazo, C., B. Tisseyre, S. Guillaume, and H. Ojeda. 2008. The potential of high spatial resolution information to define within-vineyard zones related to vine water status. Precis. Agric. 9:285–302. doi:10.1007/s11119-008-9073-1
3. Allen, R.G., L.S. Pereira, D. Raes, and M. Smith. 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. Irrig. Drain. Pap. 56. FAO, Rome.
4. Blackmer, T., J. Schepers, and G. Meyer. 1995. Remote sensing to detect nitrogen deficiency in corn. In: P.C. Robert et al., editors, Site-specific management for agricultural systems. ASA, CSSA, and SSSA, Madison WI. p. 505–512.
5. Blackmore, S. 1999. Remedial correction of yield map data. Precis. Agric. 1:53–66. doi:10.1023/A:1009969601387
6. Blackmore, S. 2000. The interpretation of trends from multiple yield maps. Comput. Electron. Agric. 26:37–51. doi:10.1016/S0168-1699(99)00075-7
7. Blackmore, S., R.J. Godwin, and S. Fountas. 2003. The analysis of spatial and temporal trends in yield map data over six years. Biosyst. Eng. 84:455–466. doi:10.1016/S1537-5110(03)00038-2
8. Box, G.E.P., and D.R. Cox. 1964. An analysis of transformations. J.R. Stat. Soc., Ser. B 26:211–252.
9. Cairns, J.E., C. Sanchez, M. Vargas, R. Ordonez, and J.L. Araus. 2012. Dissecting maize productivity: Ideotypes associated with grain yield under drought stress and well-watered conditions. J. Integr. Plant Biol. 54:1007–1020. doi:10.1111/j.1744-7909.2012.01156.x
10. Corwin, D.L., S.M. Lesch, P.J. Shouse, R. Soppe, and J.E. Ayars. 2003. Identifying soil properties that influence cotton yield using soil sampling directed by apparent soil electrical conductivity. Agron. J. 95:352–364. doi:10.2134/agronj2003.0352
11. de Franco, R., G. Biella, L. Tosi, P. Teatini, A. Lozej, B. Chiozzotto, et al. 2009. Monitoring the saltwater intrusion by time lapse electrical resistivity tomography: The Chioggia test site (Venice Lagoon, Italy). J. Appl. Geophys. 69:117–130. doi:10.1016/j.jappgeo.2009.08.004
12. Dobermann, A., and J.L. Ping. 2004. Geostatistical integration of yield monitor data and remote sensing improves yield maps. Agron. J. 96:285–297. doi:10.2134/agronj2004.0285
13. Elmetwalli, A.M.H., A.N. Tyler, P.D. Hunter, and C.A. Salt. 2012. Detecting and distinguishing moisture- and salinity-induced stress in wheat and maize through in situ spectroradiometry measurements. Remote Sens. Lett. 3:363–372. doi:10.1080/01431161.2011.599346
14. FAO-UNESCO. 1989. Soil map of the world, Revised legend. FAO, Rome.
15. Furby, S., P. Caccetta, and J. Wallace. 2010. Salinity monitoring in Western Australia using remotely sensed and other spatial data. J. Environ. Qual. 39:16–25. doi:10.2134/jeq2009.0036
16. Gitelson, A.A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 161:165–173. doi:10.1078/0176-1617-01176
17. Goovaerts, P. 1997. Geostatistics for natural resources evaluation. Oxford Univ. Press, New York.
18. Hu, Y., Z. Burucs, S. von Tucher, and U. Schmidhalter. 2007. Short-term effects of drought and salinity on mineral nutrient distribution along growing leaves of maize seedlings. Environ. Exp. Bot. 60:268–275. doi:10.1016/j. envexpbot.2006.11.003
19. Kruskal, W.H., and W.A. Wallis. 1952. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47:583–621. doi:10.1080/01621459.1952.10 483441
20. Letey, J., and A. Dinar. 1986. Simulated crop-water production functions for several crops when irrigated with saline waters. Hilgardia 54:1–32.
21. Lobell, D.B., S.M. Lesch, D.L. Corwin, M.G. Ulmer, K.A. Anderson, D.J. Potts, et al. 2010. Regional-scale assessment of soil salinity in the Red River Valley using multi-year MODIS EVI and NDVI. J. Environ. Qual. 39:35–41. doi:10.2134/jeq2009.0140
22. Lobell, D.B., J.I. Ortiz-Monasterio, F.C. Gurrola, and L. Valenzuela. 2007. Identification of saline soils with multiyear remote sensing of crop yields. Soil Sci. Soc. Am. J. 71:777–783. doi:10.2136/sssaj2006.0306
23. Maas, E. 1996. Crop salt tolerance. In: K.K. Tanji, editor, Agricultural salinity assessment and management. ASCE Manual 71. Am. Soc. Civ. Eng., New York. p. 262–304.
24. Madrigal, L.P., C.L. Wiegand, J.G. Meraz, B.D.R. Rubio, X.C. Estrada, and O.L. Ramirez. 2003. Soil salinity and its effect on crop yield: A study using satellite imagery in three irrigation districts. Ing. Hidraul. Mex. 18:83–97.
25. Manoli, G., E. Scudiero, M. Putti, F. Morari, and P. Teatini. 2014. Farmland productivity under stress conditions: A field scale monitoring and modeling study on the Venice coastland, Italy. Paper presented at: EGU General Assembly, Vienna. 27 Apr.–2 May 2014.
26. Manoli, G., S. Bonetti, E. Scudiero, P. Teatini, P.J. Binning, F. Morari, et al. 2013. Monitoring and modeling farmland productivity along the Venice coastland, Italy. Procedia Environ. Sci. 19:361–368. doi:10.1016/j. proenv.2013.06.041
27. McBratney, A., B. Whelan, T. Ancev, and J. Bouma. 2005. Future directions of precision agriculture. Precis. Agric. 6:7–23. doi:10.1007/s11119-005-0681-8
28. Mulla, D.J. 2013. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 114:358–371. doi:10.1016/j.biosystemseng.2012.08.009
29. Munns, R. 2002. Comparative physiology of salt and water stress. Plant Cell Environ. 25:239–250. doi:10.1046/j.0016-8025.2001.00808.x
30. NeSmith, D.S., and J.T. Ritchie. 1992. Effects of soil water-deficits during tassel emergence on development and yield component of maize (Zea mays L.). Field Crops Res. 28:251–256. doi:10.1016/0378-4290(92)90044-A
31. Ping, J.L., and A. Dobermann. 2005. Processing of yield map data. Precis. Agric. 6:193–212. doi:10.1007/s11119-005-1035-2
32. Raun, W.R., J.B. Solie, K.L. Martin, K.W. Freeman, M.L. Stone, G.V. Johnson, and R.W. Mullen. 2005. Growth stage, development, and spatial variability in corn evaluated using optical sensor readings. J. Plant Nutr. 28:173–182. doi:10.1081/PLN-200042277
33. Rhoades, J.D., F. Chanduvi, and S.M. Lesch. 1999. Soil salinity assessment: Methods and interpretation of electrical conductivity measurements. FAO, Rome.
34. Rouse, J.W., Jr., R.H. Haas, J.A. Schell, and D.W. Deering. 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: S.C. Freden et al., editors, Third Earth Resources Technology Satellite Symposium, Washington, DC. 10–14 Dec. 1973. NASA SP-351. U.S. Gov. Print. Office, Washington, DC. p. 309–317.
35. Scudiero, E., A. Berti, P. Teatini, and F. Morari. 2012. Simultaneous monitoring of soil water content and salinity with a low-cost capacitance-resistance probe. Sensors 12:17588–17607. doi:10.3390/s121217588
36. Scudiero, E., F. Braga, P. Teatini, D. Piragnolo, A. Berti, and F. Morari. 2011. Saltwater contamination in the Venice Lagoon margin, Italy: 2. The influence on the soil productivity: First results. In: S. Keesstra and G. Mol, editors, Soil science in a changing world: Wageningen Conference on Applied Soil Science, Wageningen, the Netherlands. 18–22 Sept. 2011. Wageningen Agric. Univ., Wageningen, the Netherlands. p. 95.
37. Scudiero, E., P. Teatini, F. Braga, and F. Morari. 2014. Saltwater contamination and soil productivity at the southern margin of the Venice Lagoon, Italy. In D. Suarez and A. Dinar, editors, Salinity Forum 2014, Riverside, CA. 16–18 June 2014. Univ. of California, Riverside. p. 294–295.
38. Scudiero, E., P. Teatini, D.L. Corwin, R. Deiana, A. Berti, and F. Morari. 2013. Delineation of site-specific management units in a saline region at the Venice Lagoon margin, Italy, using soil reflectance and apparent electrical conductivity. Comput. Electron. Agric. 99:54–64. doi:10.1016/j. compag.2013.08.023
39. Shanahan, J.F., J.S. Schepers, D.D. Francis, G.E. Varvel, W.W. Wilhelm, J.M. Tringe, et al. 2001. Use of remote-sensing imagery to estimate corn grain yield. Agron. J. 93:583–589. doi:10.2134/agronj2001.933583x
40. Sharp, R., and W. Davies. 1985. Root growth and water uptake by maize plants in drying soil. J. Exp. Bot. 36:1441–1456. doi:10.1093/jxb/36.9.1441
41. Solari, F. 2006. Developing a crop based strategy for on-the-go nitrogen management in irrigated cornfields. Univ. of Nebraska, Lincoln.
42. Solari, F., J. Shanahan, R. Ferguson, J. Schepers, and A. Gitelson. 2008. Active sensor reflectance measurements of corn nitrogen status and yield potential. Agron. J. 100:571–579. doi:10.2134/agronj2007.0244
43. Tweed, S.O., M. Leblanc, J.A. Webb, and M.W. Lubczynski. 2007. Remote sensing and GIS for mapping groundwater recharge and discharge areas in salinity prone catchments, southeastern Australia. Hydrogeol. J. 15:75–96. doi:10.1007/s10040-006-0129-x
44. Vina, A., A.A. Gitelson, D.C. Rundquist, G. Keydan, B. Leavitt, and J. Schepers. 2004. Monitoring maize (Zea mays L.) phenology with remote sensing. Agron. J. 96:1139–1147. doi:10.2134/agronj2004.1139
45. Weber, V., J. Araus, J. Cairns, C. Sanchez, A. Melchinger, and E. Orsini. 2012. Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crops Res. 128:82–90. doi:10.1016/j.fcr.2011.12.016
46. Whelan, B., and A. McBratney. 2000. The “null hypothesis” of precision agriculture management. Precis. Agric. 2:265–279. doi:10.1023/A:1011838806489
47. Wolfe, D., D. Henderson, T. Hsiao, and A. Alvino. 1988. Interactive water and nitrogen effects on senescence of maize: I. Leaf area duration, nitrogen distribution, and yield. Agron. J. 80:859–864. doi:10.2134/agronj1988.0 0021962008000060004x