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Variable Rate Seeding in Precision Agriculture: Recent Advances and Future Perspectives

Author

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  • Egidijus Šarauskis

    (Institute of Agricultural Engineering and Safety, Agriculture Academy, Vytautas Magnus University, Studentu Str. 15A, Kaunas Distr., LT-53362 Akademija, Lithuania)

  • Marius Kazlauskas

    (Institute of Agricultural Engineering and Safety, Agriculture Academy, Vytautas Magnus University, Studentu Str. 15A, Kaunas Distr., LT-53362 Akademija, Lithuania)

  • Vilma Naujokienė

    (Institute of Agricultural Engineering and Safety, Agriculture Academy, Vytautas Magnus University, Studentu Str. 15A, Kaunas Distr., LT-53362 Akademija, Lithuania)

  • Indrė Bručienė

    (Institute of Agricultural Engineering and Safety, Agriculture Academy, Vytautas Magnus University, Studentu Str. 15A, Kaunas Distr., LT-53362 Akademija, Lithuania)

  • Dainius Steponavičius

    (Institute of Agricultural Engineering and Safety, Agriculture Academy, Vytautas Magnus University, Studentu Str. 15A, Kaunas Distr., LT-53362 Akademija, Lithuania)

  • Kęstutis Romaneckas

    (Institute of Agroecosystems and Soil Science, Agriculture Academy, Vytautas Magnus University, Studentu Str. 11, Kaunas Distr., LT-53361 Akademija, Lithuania)

  • Algirdas Jasinskas

    (Institute of Agricultural Engineering and Safety, Agriculture Academy, Vytautas Magnus University, Studentu Str. 15A, Kaunas Distr., LT-53362 Akademija, Lithuania)

Abstract

The main objective of this study was to analyze variable rate seeding (VRS) methods and critically evaluate their suitability and effectiveness for the challenges under field conditions. A search was performed using scientific databases and portals by identifying for analysis and evaluation 92 VRS methodologies, their impact and economic benefits depending on the main parameters of the soil and environment. The results of the review identified that VRS could adapt the appropriate seeding rate for each field zone, which was based on site-specific data layers of soil texture, ECa, pH and yield maps. Then, remotely detected images or other data which identify yield-limiting factors were identified. The site-specific sowing method (with a variable sowing rate for each field area) allows the optimization of crop density to obtain the best agronomic and economic results. Various proximal and remote sensor systems, contact and contactless equipment, mapping and VRS modeling technologies are currently used to determine soil and crop variability. VRS depends on the field characteristics’ sowing equipment capabilities, the planned harvest, soil productivity and machine technology interactions with the environment. When forecasting the effective payback of a VRS over the desired period, the farm size should on average be at least 150 ha. In future studies, to achieve the best solutions and optimal methods, it is important to test, evaluate and put into practice the latest methodologies on farms, to perform complex assessments of changes in sensor, soil, plant and environmental parameters.

Suggested Citation

  • Egidijus Šarauskis & Marius Kazlauskas & Vilma Naujokienė & Indrė Bručienė & Dainius Steponavičius & Kęstutis Romaneckas & Algirdas Jasinskas, 2022. "Variable Rate Seeding in Precision Agriculture: Recent Advances and Future Perspectives," Agriculture, MDPI, vol. 12(2), pages 1-24, February.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:305-:d:754341
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    References listed on IDEAS

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    1. Lowenberg-DeBoer, James, 1998. "Economics Of Variable Rate Planting For Corn," Staff Papers 28685, Purdue University, Department of Agricultural Economics.
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    Cited by:

    1. Marius Kazlauskas & Indrė Bručienė & Dainius Savickas & Vilma Naujokienė & Sidona Buragienė & Dainius Steponavičius & Kęstutis Romaneckas & Egidijus Šarauskis, 2023. "Life Cycle Assessment of Winter Wheat Production Using Precision and Conventional Seeding Technologies," Sustainability, MDPI, vol. 15(19), pages 1-13, September.
    2. Hasan Mirzakhaninafchi & Manjeet Singh & Anoop Kumar Dixit & Apoorv Prakash & Shikha Sharda & Jugminder Kaur & Ali Mirzakhani Nafchi, 2022. "Performance Assessment of a Sensor-Based Variable-Rate Real-Time Fertilizer Applicator for Rice Crop," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
    3. Dongxu Su & Weixiang Yao & Fenghua Yu & Yihan Liu & Ziyue Zheng & Yulong Wang & Tongyu Xu & Chunling Chen, 2022. "Single-Neuron PID UAV Variable Fertilizer Application Control System Based on a Weighted Coefficient Learning Correction," Agriculture, MDPI, vol. 12(7), pages 1-22, July.

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