Structural carbohydrate and lignin content of perennial cereal forage grasses depending on the growth phase and digital innovations in forage composition analysis
https://doi.org/10.26897/2949-4710-2023-4-107-115
Abstract
The article is devoted to the study of the content of cell wall carbohydrates – neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL) – in cereal forage grasses depending on the growth phases. An increase in all cell wall fractions was revealed as the growth phases changed. The content of acid detergent fiber, neutral-detergent fiber and acid detergent lignin (% in dry matter) in cereal forage grasses (awnless brome, meadow fescue, meadow timothy) is 31‑32, 50‑55, 4‑6 before earing; in the earing phase – 32‑37, 55‑65, and 5‑6; in the flowering phase – 40‑45, 70‑72, 7‑9, respectively. As grasses grow, their composition changes with the accumulation of cell walls. The increase in the proportion of neutral detergent fiber occurs mainly due to an increase in the proportion of cellulose and a decrease in the proportion of hemicellulose, which is consistent with a higher digestibility of grasses in the early growth stages. According to the results of the study, the relationship between crude fiber and acid detergent fiber was closer (n = 64, s = 2.4%, r = 0.93) than between crude fiber and neutral detergent fiber (n = 64, s = 4.4%, r = 0.87). As the composition of grass changes daily during the growing season, it is advisable to determine its composition as soon as possible. As chemical methods are time-consuming, an express method based on an infrared analyzer can be used to solve this problem. The express method involves, after the grinding of the sample, sequential operations such as calibrating the analyzer, placing the sample in the analyzer and analyzing the samples with the analyzer. This means that this method is also quite time-consuming. To obtain information more quickly (two hours), digital technologies are now increasingly being used. The method based on digital technologies involves the sequential execution of the following operations: UAV launch, crop survey, transmission of multispectral data to the server, information processing and calculation of crude fiber content.
About the Authors
Н. К. KhudyakovaRussian Federation
Hatima K. Khudyakova, CSc (Agr), Leading Research Associate
Building 1, Scientific town, Lobnya, Moscow region, 141055
Е. V. Khudyakova
Russian Federation
Elena V. Khudyakova, DSc (Econ), Professor, Professor at the Department of Applied Information Science
49, Timiryazevskaya Str., Moscow, 127434
М. N. Stepantsevich
Russian Federation
Marina N. Stepantsevich, CSc (Econ), Associate Professor, Associate Professor at the Department of Applied Information Science
49, Timiryazevskaya Str., Moscow, 127434
О. А. Motorin
Russian Federation
OlegA. Motorin, CSc (Polit), Associate Professor at the Department of Applied Information Science
49, Timiryazevskaya Str., Moscow, 127434
М. V. Jouravlev
Russian Federation
Mikhail V. Jouravlev, CSc (Phys-math), Associate Professor at the Department of Applied Information Science
49, Timiryazevskaya Str., Moscow, 127434
М. S. Nikanorov
Russian Federation
Mikhail S. Nikanorov, Senior Lecturer at the Department of Applied Information
Science
49, Timiryazevskaya Str., Moscow, 127434
References
1. Bailey R.W. Structural carbohydrates. Chemistry and biochemistry of herbage. New York: “Academic Prеss”, 1973:157-211.
2. Khudyakova E.V., Khudyakova H.K., Shitikova A.V., Savoskina O.A., Konstantinovich A.V. Information technologies for determination the optimal period of preparing fodder from perennial grasses. Periodico Tche Quimica. 2020;17(35):1044-1056. https://doi.org/10.52571/PTQ.v17.n35.2020.86_KHUDYAKOVA_pgs_1044_1056.pdf
3. Van-Soest P.J., Robertson J.B., Lewis B.A. Methods for Dietary Fiber, Neutral Detergent Fiber, and Nonstarch Polysaccharides in Relation to Animal Nutrition. J. Dairy Sci. 1991;74:3583-3597.
4. National Research Council. 1982. United States-Canadian Tables of Feed Composition: Nutritional Data for United States and Canadian Feeds, Third Revision. Washington, DC: The National Academies Press. https://doi.org/10.17226/1713
5. Organizational and technological requirements for milk production at industrial-type dairy complexes: Resolution of the Ministry of Agriculture and Food of the Republic of Belarus No. 16 of 4 June 2018. (In Russ.) URL: https://brestplem.by/informatsiya/instruktsii/oot.pdf
6. National Research Council. Nutrient Requirements of Dairy Cattle: Seventh Revised Edition. Washington, DC: The National Academies Press, 2001. https://doi.org/10.17226/9825
7. Walelegne M., Meheret F., Derseh M.B., Dejene M., Asmare Y.T., Prasad K.V.S.V., Jones C.S., Dixon R.M., DuncanA.J. Near-infrared reflectance spectroscopy using a portable instrument to measure the nutritive value of oilseed meals as livestock feed. Front. Anim. Sci. 2023;4:1203449. https://doi.org/10.3389/fanim.2023.1203449
8. Lingjie Zeng, Chengci Chen. Using remote sensing to estimate forage biomass and nutrient contents at different growth stages. Biomass and Bioenergy. 2018;115:74-81. https://doi.org/10.1016/j.biombioe.2018.04.016
9. Khudyakova E.V., Gorbachev M.S., Nifontova Е.A. Improving the efficiency of agro-industrial complex management based on digitalization and system approach. IOP Conf. Ser.: Earth Environ. Sci. 2018;274:012079. https://doi.org/10.1088/1755-1315/274/1/012079
10. Khudyakova E., Nikanorov M., Bystrenina I., Cherevatova T., Sycheva I. Forecasting the production of gross output in agricultural sector of the ryazan oblast. Estudios de Economía Aplicada. 2021;39;6. https://doi.org/10.25115/eea.v39i6.5171
11. Slastya I., Khudyakova E., Vasenev I., Nikanorov M., Fomina T. Improvement of the Integral Indicator of the Ecological and Toxicological Assessment of the Danger of the Use of Pesticides in Agriculture and the Creation of an Information System for Their Monitoring. Agriculture (Switzerland). 2023;13(9):1797. https://doi.org/10.3390/agricultural13091797
12. Khudyakova E.V., Kushnareva M.N., Gorbachev M.I. Determining the optimal timing of feed procurement based on the use of Internet of Things (IoT) technologies. Glavniy agronom. 2022;2. (In Russ.)
13. Yao X., Zhu Y., Tian Y. et al. Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. International Journal of Applied Earth Observation and Geoinformation. 2010;12:89 100. https://doi.org/10.1016/j.jag.2009.11.008
14. Zhang X., Liu F., He Y., Gong X. Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging. Biosystems Engineering. 2013;115:56 65. https://doi.org/10.1016/j.biosystemseng.2013.02.007
15. Trukhachev V.I., Sycheva O.V., Starodubtseva G.P., Veselova M.V. Technology of milk herbal tea “Stevilakt”. Pishchevaya industriya. 2012;2:18-20. (In Russ.)
Review
For citations:
Khudyakova Н.К., Khudyakova Е.V., Stepantsevich М.N., Motorin О.А., Jouravlev М.V., Nikanorov М.S. Structural carbohydrate and lignin content of perennial cereal forage grasses depending on the growth phase and digital innovations in forage composition analysis. Timiryazev Biological Journal. 2023;1(4):107-115. (In Russ.) https://doi.org/10.26897/2949-4710-2023-4-107-115