Skip to main content

Advertisement

SpringerLink
Log in

Identification of management zones with different potential moisture availability for sustainable intensification of dryland agriculture

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

In semiarid environments moisture availability is the primary factor that controls land productivity, however, no method to differentiate landscape portions with high moisture availability has yet been proposed for delimitation of crop management zones for precision agriculture. The objective of the present study was to develop a methodology to determine sites with different soil potential moisture availability to improve the delimitation of homogeneous crop management zones in semiarid environments. An altimetric survey was carried out in the field to obtain a DEM with a spatial resolution of 5 m. Subsequently, maps of slopes, area of ​​flow accumulation, sub-basins of the Topographic Wetness index (TWI) were made. A potential moisture availability (PMA) map was generated by linking the TWI map with a map of previously reclassified sub-basins and homogeneous PMA zones were delineated. Soil profiles were sampled on transects through the PMA zones, and during three growing seasons soil moisture contents were recorded. The PMA zones had homogeneous soil types and moisture contents and differed from each other in soil profile depth and available moisture contents, especially in the more humid season. Soil moisture correlated well with the antecedent precipitation index (API) during crop growth and in the PMA zones with higher altimetry, while only weak relationships were found during fallow periods and for the lowest altimetry PMA zone. The proposed methodology was useful for identifying landscape portions with differences in potential moisture availability as the spatial-temporal variability was represented. The use of the API combined with the potential moisture availability allowed a better fit in its relationship with the soil available moisture contents (AMC).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Bennie, A. T. P., & Hensley, M. (2001). Maximizing precipitation utilization in dryland agriculture in South Africa — a review. Journal of Hydrology, 241, 124–139.

    Article  Google Scholar 

  • Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrological Sciences Bulletin, 24, 43–69. https://doi.org/10.1080/02626667909491834

    Article  Google Scholar 

  • Blake, G. R., & Hartge, K. H. (1986). Bulk density. in: A., K. (Ed.), Methods of Soil Analysis. Part 1. Physical and minerological methods (pp. 363–382). Madison.: SSSA.

    Google Scholar 

  • Bongiovanni, R., & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision Agriculture 359–387.

  • Brocca, L., Melone, F., Moramarco, T., & Morbidelli, R. (2009). Soil moisture temporal stability over experimental areas in Central Italy. Geoderma, 148, 364–374. https://doi.org/10.1016/j.geoderma.2008.11.004

    Article  Google Scholar 

  • Cantón, Y., Del Barrio, G., Solé-Benet, A., & Lázaro, R. (2004). Topographic controls on the spatial distribution of ground cover in the Tabernas badlands of SE Spain. Catena, 55, 341–365. https://doi.org/10.1016/S0341-8162(03)00108-5

    Article  Google Scholar 

  • Caviglia, O. P., Sadras, V. O., & Andrade, F. H. (2004). Intensification of agriculture in the south-eastern Pampas: I. capture and efficiency in the use of water and radiation in double-cropped wheat-soybean. Field Crops Research, 87, 117–129. https://doi.org/10.1016/j.fcr.2003.10.002

    Article  Google Scholar 

  • Chen, J. S., Li, L., Wang, J. Y., Barry, D. A., Sheng, X. F., Gu, W. Z., Zhao, X., & Chen, L. (2004). Groundwater maintains dune landscape. Nature, 432, 459–460. https://doi.org/10.1038/432459a

    Article  CAS  PubMed  Google Scholar 

  • Cook, F. J. (2007). Chapter 80. Unsaturated Hydraulic Conductivity: Laboratory Tension Infiltrometer. In M. R. Carter, & E. G. Gregorich (Eds.), Soil sampling and methods of analysis. Canadian Society of Soil Science.

  • Crozier, M. J., Eyles, R. J., Crozier, M. J., McConchie, J. A., & Owen, R. C. (1980). Distribution of landslips in the Wairarapa hill country. New Zealand Journal of Geology and Geophysics, 23, 575–586. https://doi.org/10.1080/00288306.1980.10424129

    Article  Google Scholar 

  • de Mers, M. N. (2002). GIS modeling in raster. New York: John Wiley & Sons.

    Google Scholar 

  • Di Rienzo, J. A. A., Casanoves, F., Balzarini, M. G. G., González, L., Tablada, M., & Robledo, C. W. W. (2017). Grupo InfoStat. Argentina: FCA, Universidad Nacional de Córdoba.

    Google Scholar 

  • Dyer, J. M. (2009). Assessing topographic patterns in moisture use and stress using a water balance approach. Landscape Ecology, 24, 391–403. https://doi.org/10.1007/s10980-008-9316-6

    Article  Google Scholar 

  • Gaggioli, C., Noellemeyer, E., & Quiroga, A. (2014). Productividad y eficiencia de uso de agua de cereales invernales en dos suelos contraste de la región semiárida pampeana. Actas XXIV Congreso Argentino de la Ciencia del Suelo y II Reunión Nacional Materia Orgánica y Sustancias Húmicas Asociación Argentina Ciencia del Suelop2011–2012.

  • Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science (New York N Y), 327, 828–831. https://doi.org/10.1126/science.1183899

    Article  CAS  PubMed  Google Scholar 

  • Gómez-Plaza, A., Martínez-Mena, M., Albaladejo, J., & Castillo, V. M. (2001). Factors regulating spatial distribution of soil water content in small semiarid catchments. Journal of Hydrology, 253, 211–226. https://doi.org/10.1016/S0022-1694(01)00483-8

    Article  Google Scholar 

  • Gregoret, M. C., Díaz Zorita, M., Dardanelli, J., & Bongiovanni, R. G. (2011). Regional model for nitrogen fertilization of site-specific rainfed corn in haplustolls of the central Pampas, Argentina. Precision Agriculture, 12, 831–849. https://doi.org/10.1007/s11119-011-9224-7

    Article  Google Scholar 

  • Gruber, S., & Peckham, S. (2009). Chapter 7. Land-surface parameters and objects in hydrology. In: Developments in Soil Science. pp. 171–194. https://doi.org/10.1016/S0166-2481(08)00007-X

  • Huggins, D. R., Randall, G. W., & Russelle, M. P. (2001). Subsurface drain losses of water and nitrate following conversion of perennials to row crops. Agronomy Journal, 93, 477–486.

    Article  Google Scholar 

  • Kahlown, M., & Ashraf, M. (2005). Effect of shallow groundwater table on crop water requirements and crop yields. Agricultural Water Management, 76, 24–35. https://doi.org/10.1016/j.agwat.2005.01.005

    Article  Google Scholar 

  • Klute, A. (1986). Water Retention: Laboratory Methods, in: Methods of Soil Analysis: Part 1—Physical and Mineralogical Methods. Soil Science Society of America, American Society of Agronomy, pp. 635–662. https://doi.org/10.2136/sssabookser5.1.2ed.c26

  • Kroetsch, D., & Wang, C. (2007). Particle size distribution. In M. R. Carter, & E. G. Gregorich (Eds.), Soil sampling and methods of analysis. Canadian Society of Soil Science, Boca Raton FL USA: Taylor and Francis.

    Google Scholar 

  • Kumhálová, J., Kumhála, F., Kroulík, M., & Matějková, Š. (2011). The impact of topography on soil properties and yield and the effects of weather conditions. Precision Agriculture, 12, 813–830. https://doi.org/10.1007/s11119-011-9221-x

    Article  Google Scholar 

  • Latron, J., & Gallart, F. (2008). Runoff generation processes in a small Mediterranean research catchment (Vallcebre, Eastern Pyrenees). Journal of Hydrology, 358, 206–220. https://doi.org/10.1016/j.jhydrol.2008.06.014

    Article  Google Scholar 

  • Linsley, R. K., & Kohler, M. A. (1951). Variations in storm rainfall over small areas. Eos, Transactions American Geophysical Union 32, 245–250. https://doi.org/10.1029/TR032i002p00245

  • Meyer, N., Bergez, J.-E., Constantin, J., & Justes, E. (2019). Cover crops reduce water drainage in temperate climates: A meta-analysis. Agronomy for Sustainable Development, 39(1), 3. https://doi.org/10.1007/s13593-018-0546-y

  • Moore, I. D., Gessler, P., Nielsen, G. A. E., & Peterson, G. (1993). Soil attribute prediction using terrain analysis. Soil Science Society of America Journal - SSSAJ, 57. https://doi.org/10.2136/sssaj1993.572NPb

  • Nasta, P., & Gates, J. B. (2013). Plot-scale modeling of soil water dynamics and impacts of drought conditions beneath rainfed maize in Eastern Nebraska. Agricultural Water Management, 128, 120–130.

    Article  Google Scholar 

  • National Soil Survey Center. (2012). Field Book for describing and sampling soils. USDA NRCS.

  • Noellemeyer, E., Fernández, R., & Quiroga, A. (2013). Crop and tillage effects on water productivity of dryland agriculture in Argentina. Agriculture, 3, 1–11. https://doi.org/10.3390/agriculture3010001

    Article  Google Scholar 

  • Nosetto, M. D., Jobbágy, E. G., Jackson, R. B., & Sznaider, G. (2009). Reciprocal influence of crops and shallow ground water in sandy landscapes of the Inland Pampas. Field Crops Research, 113, 138–148. https://doi.org/10.1016/j.fcr.2009.04.016

    Article  Google Scholar 

  • NRCS Soil Survey Staff (2014). United States Department of Agriculture Keys to Soil Taxonomy.

  • Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & Core Team, R. (2021). _nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1–153, https://CRAN.R-project.org/package=nlme

  • Qiu, Y., Fu, B., Wang, J., Chen, L., Meng, Q., & Zhang, Y. (2010). Spatial prediction of soil moisture content using multiple-linear regressions in a gully catchment of the Loess Plateau, China. Journal of Arid Environments, 74, 208–220. https://doi.org/10.1016/j.jaridenv.2009.08.003

    Article  Google Scholar 

  • Core Team, R. (2020). R: a language and environment for statistical computing. [WWW Document]. Vienna, Austria: R Foundation for Statistical Computing.

    Google Scholar 

  • R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/

  • Roudier, P., Tisseyre, B., Poilvé, H., & Roger, J. M. (2008). Management zone delineation using a modified watershed algorithm. Precision Agriculture, 9, 233–250. https://doi.org/10.1007/s11119-008-9067-z

    Article  Google Scholar 

  • Sittner, W. T., Schauss, C. E., & Monro, J. C. (1969). Continuous hydrograph synthesis with an API-type hydrologic model. Water Resources Research, 5, 1007–1022. https://doi.org/10.1029/WR005i005p01007

    Article  Google Scholar 

  • Skjemstad, J. O., & Baldock, J. A. (2007). Chapter 21 Total and Organic Carbon. In M. R. Carter, & E. G. Gregorich (Eds.), Soil sampling and methods of analysis. Canadian Society of Soil Science.

  • Soil Survey Staff. (2014). Keys to Soil Taxonomy (12th ed.). USDA NRCS.

  • Stroosnijder, L., Moore, D., Alharbi, A., Argaman, E., Biazin, B., & van den Elsen, E. (2012). Improving water use efficiency in drylands. Current Opinion in Environmental Sustainability, 4, 497–506. https://doi.org/10.1016/j.cosust.2012.08.011

    Article  Google Scholar 

  • Tonitto, C., David, M. B., & Drinkwater, L. E. (2006). Replacing bare fallows with cover crops in fertilizer-intensive cropping systems: a meta-analysis of crop yield and N dynamics. Agriculture Ecosystems & Environment, 112, 58–72. https://doi.org/10.1016/j.agee.2005.07.003

    Article  Google Scholar 

  • Tromp-van Meerveld, H. J., & McDonnell, J. J. (2006). Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis. Water Resources Research, 42, https://doi.org/10.1029/2004WR003800

  • Vitharana, U. W., Van Meirvenne, M., Simpson, D., Cockx, L., & De Baerdemaeker, J. (2008). Key soil and topographic properties to delineate potential management classes for precision agriculture in the european loess area. Geoderma, 143, 206–215. https://doi.org/10.1016/j.geoderma.2007.11.003

    Article  CAS  Google Scholar 

  • Western, A. W., & Grayson, R. B. (1998). The Tarrawarra Data Set: soil moisture patterns, soil characteristics, and hydrological flux measurements. Water Resources Research, 34, 2765–2768. https://doi.org/10.1029/98WR01833

    Article  Google Scholar 

  • Western, A. W., Zhou, S. L., Grayson, R. B., McMahon, T. A., Blöschl, G., & Wilson, D. J. (2004). Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. Journal of Hydrology, 286, 113–134. https://doi.org/10.1016/j.jhydrol.2003.09.014

    Article  Google Scholar 

  • Whelan, B. M., & McBratney, A. B. (2000). The “Null Hypothesis” of Precision Agriculture Management. Precision Agriculture, 2, 265–279.

    Article  Google Scholar 

  • Wittwer, R. A., Dorn, B., Jossi, W., & van der Heijden, M. G. A. (2017). Cover crops support ecological intensification of arable cropping systems. Scientific Reports, 7, https://doi.org/10.1038/srep41911

  • Wood, E. F., Lettenmaier, D. P., & Zartarian, V. G. (1992). A land-surface hydrology parameterization with subgrid variability for general circulation models. Journal of Geophysical Research: Atmospheres, 97, 2717–2728. https://doi.org/10.1029/91JD01786

    Article  Google Scholar 

  • Zehe, E., Graeff, T., Morgner, M., Bauer, A., & Bronstert, A. (2010). Plot and field scale soil moisture dynamics and subsurface wetness control on runoff generation in a headwater in the Ore Mountains. Hydrology and Earth System Sciences, 14, 873–889. https://doi.org/10.5194/hess-14-873-2010

    Article  Google Scholar 

  • Zhao, B., Dai, Q., Han, D., Dai, H., Mao, J., Zhuo, L., et al. (2019). Estimation of soil moisture using modified antecedent precipitation index with application in landslide predictions. Landslides, 16, 2381–2393. https://doi.org/10.1007/s10346-019-01255-y

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. INTA (Argentinean Institute for Agricultural Technology), Anguil Experimental Station, Anguil La Pampa, Argentina

    Mauricio Farrell

  2. CONICET (National Council for Science and Technology), Godoy Cruz 2290, Buenos Aires, Argentina

    Emmanuel Leizica

  3. Facultad de Agronomía, National University of La Pampa, Ruta 35 km 334, Santa Rosa, RA 6300, La Pampa, Argentina

    Adriana Gili & Elke Noellemeyer

Authors
  1. Mauricio Farrell
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel Leizica
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adriana Gili
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elke Noellemeyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elke Noellemeyer.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farrell, M., Leizica, E., Gili, A. et al. Identification of management zones with different potential moisture availability for sustainable intensification of dryland agriculture. Precision Agric 24, 1116–1131 (2023). https://doi.org/10.1007/s11119-023-10002-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-023-10002-2

Keywords

Search

Navigation