Landscape drivers of phytomass variation on agricultural lands in the Orenburg Cis-Urals

Predictability and low intra-field variability of yields are the key interest of agriculture. The dependence of productivity on hydrothermal conditions could be determined by the spatial structure of landscape. We used the example of a steppe agricultural landscape in the Orenburg region to study the influence of the areal and qualitative characteristics of the landscape neighborhood on the intra-field variability of phytomass. We used vegetation indices (NDVI) for 92 field sites with a typical area of 100 hectares based on 42 Landsat satellite images over a 30-year period, with an average of 4–6 images per season. The NDVI of field pixels was considered as a dependent variable, while the areas of erosion landforms and live forest belts as well as the total NDVI of forest belts within the distance of 150 m were independent ones. Types of crops and the properties of forest belts and hollows were described during field studies. Cluster analysis was used to recognize crop classes based on the similarity of the annual NDVI variations to those during the years of field observations. Multiple regression equations were compiled to calculate the total contribution of three neighborhood factors to the intra-field variation in phytomass, as well as their individual contributions. We compared the parameters of the equations for June dates with different heat and moisture supply over three years. We found that the intra-field variation in green phytomass could be for more than a half determined by proximity to erosion forms and forest belts. The fields were classified according to the stability of the influence of forest belts and hollows under different hydrothermal conditions. The most stable influence on the green phytomass of crops is correlated with a proximity to erosion landforms, the least – with the state of forest belts. The dependence of green phytomass on landscape neighborhood was stable from year to year and highly significant for 44% of the study area. The proximity to forest belts and their state more strongly influence the June phytomass during hot and dry years, and proximity to erosion forms during years with abundant winter and spring precipitation. The closer to forest belts, the greater is phytomass in almost all fields. The area of adjacent live forest stands is more important than their state. Threshold values have been established for neighborhoods with erosion landforms and live forest belts, which control the green phytomass increase within the interfluves. A significant influence of neighborhood factors on phytomass is manifested regardless of the crop type.

1. Badreev R.M. Vlijanie normy vyseva, sposobov vnesenija i urovnja azotnogo pitanija na urozhajnost’ i kachestvo zerna mnogorjadnogo i dvurjadnogo jachmenja na chernozemah juzhnyh orenburgskogo Predural’ja [The influence of seeding rate, application methods and nitrogen nutrition level on the yield and grain quality of multi-row and two-row barley on southern chernozems of the Orenburg Cis-Urals], Ph.D. Thesis, Orenburg, 2008, 210 p. (In Russian)

2. Birtwistle A.M., Laituri M., Bledsoe B. et al. Using NDVI to measure precipitation in semi-arid landscapes, Journal of Arid Environments, 2016, vol. 131, p. 15–24.

3. Blackmore S., Godwin R.J., Fountas S. The analysis of spatial and temporal trends in yield map data over six years, Biosystems Engineering, 2003, vol. 84, p. 455–466, DOI: 10.1016/S1537-5110(03)00038-2.

4. Cheremisinov A.Yu., Spakhova A.S. Agrolesomelioratsiya [Agroforestry], Voronezh, VGAU Publ., 2014, 212 p. (In Russian)

5. Corwin D. Site-specific management and delineating management zones, Precision Agriculture for Food Security and Environmental Protection, M. Oliver (еd.), Earthscan, London, UK, 2013, р. 135–157.

6. Detsch F., Otte I., Appelhans T. et al. Seasonal and longterm vegetation dynamics from 1-km GIMMS-based NDVI time series at Mt. Kilimanjaro, Tanzania, Remote Sensing of Environment, 2016, vol. 178, p. 70–83, DOI: 10.1016/j.rse.2016.03.007.

7. Duan T., Chapman S.C., Guo Y. et al. Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle, Field Crops Research, 2017, vol. 210, p. 71–80, DOI: 10.1016/j.fcr.2017.05.025.

8. Eckert S., Hüsler F., Liniger H. et al. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia, Journal of Arid Environments, 2015, vol. 113, p. 16–28, DOI: 10.1016/j.jaridenv.2014.09.001.

9. Episheva Yu.Yu. Struktura urozhaya i urozhaynost’ zernovykh kul’tur na chernozemakh yuzhnykh Orenburgskoy oblasti [Structure of crop yield and productivity of grain crops on southern chernozem of the Orenburg region], Rol’ agronomicheskoy nauki v optimizatsii tekhnologiy vozdelyvaniya sel’skokhozyaystvennykh kul’tur [The role of agronomy in optimization of the crop cultivation technologies], Izhevsk, 2020, p. 136–139. (In Russian)

10. Glazunov G.P., Gendugov V.M., Evdokimova M.V. et al. [Selection and testing of a model of seasonal dynamics of crop biomass using the vegetation indices], Materialy II Vserossiyskoy nauchnoy konferentsii “Primenenie sredstv distantsionnogo zondirovaniya Zemli v sel’skom khozyaystve” [Materials of the II All-Russian Scientific Conference “Application of Earth Remote Sensing in Agriculture”], Sankt-Peterburg, FGBNU AFI Publ., 2018, p. 75–84. (In Russian)

11. Gulyanov Yu.A. Monitoring fitometricheskikh parametrov s ispol’zovaniem innovatsionnykh metodov skanirovaniya posevov [Monitoring of phytometric parameters using innovative methods of scanning crops], Tavricheskiy vestnik agrarnoy nauki, 2019, vol. 3, no. 19, p. 64–76. DOI: 10.33952/2542-0720-2019-3-19-64-76. (In Russian)

12. Johansen B., Tømmervik H. The relationship between phytomass, NDVI and vegetation communities on Svalbard, International Journal of Applied Earth Observation and Geoinformation, 2014, vol. 27, p. 20–30, DOI: 10.1016/j.jag.2013.07.001.

13. Khoroshev A.V., Tkach K.A., Murtazina D.U. Vliyanie landshaftnoy struktury na urozhaynost’ zernovykh kul’tur v stepnoy zone Kazakhstana [The influence of landscape structure on the yield of grain crops in the steppe zone of Kazakhstan], Vestnik Moskovskogo Universiteta, Ser. 5, Geogr., 2018, vol. 73, no. 3, p. 62–69. (In Russian)

14. Kiryanov-Gref F.K., Khoroshev A.V., Anatskaya K.A. Factors of intra-field phytomass variability in steppe agricultural landscapes of Kazakhstan, Bulletin of the Karaganda University, “Biology. Medicine. Geography” Series, 2024, vol. 29, iss. 1(113), p. 150–158, DOI: 10.31489/2024BMG1/150-158.

15. Kiryushin V.I. Ekologicheskie osnovy proektirovaniya sel’skokhozyaystvennykh landshaftov [Ecological principles of designing agricultural landscapes.], Sankt-Peterburg, KVADRO Publ., 2018, 568 p. (In Russian)

16. Kliment’ev A.I. [Soil-geographical zoning of the Orenburg region], Voprosy stepevedeniya [Issues in steppe studies], Orenburg, 2005, p. 83–95. (In Russian)

17. Lyle G., Lewis M. Ostendorf B. Testing the temporal ability of LANDSAT imagery and precision agriculture technology to provide high resolution historical estimates of wheat yield at the farm scale, Remote Sensing, 2013, vol. 5, p. 1549–1567, DOI: 10.3390/rs5041549.

18. Mahajan U., Bundel B.R. Drones for normalized difference vegetation index (NDVI), to estimate crop health for precision agriculture: A cheaper alternative for spatial satellite sensors, Proceedings of the International Conference on Innovative Research in Agriculture, Food Science, Forestry, Horticulture, Aquaculture, Animal Sciences, Biodiversity, Ecological Sciences and Climate Change (AFHABEC-2016), Delhi, India, 2016, vol. 22, p. 31.

19. Manaenkov A.S., Podgaetskaya P.M., Popov V.S. Vliyanie polezashchitnykh lesnykh polos na razvitie yarovoy pshenitsy v priopushechnoy zone posevov [The influence of forest belts on the development of spring wheat in the edge zone of crops], Lomonosov Geography Journal, 2023, vol. 78, no. 4, p. 97–106, DOI: 10.55959/MSU0579-9414.5.78.4.9. (In Russian)

20. Pis’man T.I., Botvich I.Yu., Sid’ko A.F. Opredelenie sezonnoy dinamiki urozhaynosti agrotsenozov na osnove sputnikovoy informatsii i matematicheskoy modeli [Determination of the seasonal dynamics of the yield of agrocenoses based on satellite information and a mathematical model], Izv. Akad. Nauk, Ser. Biol., 2014, no. 2, p. 196–202, DOI: 10.7868/s0002332914020106. (In Russian)

21. Verhulst N., Govaerts B. The normalized difference vegetation index (NDVI) Green Seeker TM handheld sensor: Toward the integrated evaluation of crop management, Part A, Concepts and case studies, Mexico, D.F., 2010, DOI: 10.1016/j.funeco.2023.101233.