Open Journal Systems

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Agriculture, sustainable development, environment, farmer knowledge and universities

Mirela Rizo-Mustelier, Daniel Rafael Vuelta-Lorenzo, Ana María Lorenzo-García

Article ID: 2075
Vol 3, Issue 2, 2022

DOI: https://doi.org/10.54517/ama.v3i2.2075
VIEWS - 3326 (Abstract)

Download PDF

Abstract

Society still faces serious environmental and socioeconomic problems, which have only been partially offset by the rapid development of knowledge, information technology, and technology; however, sustainable development remains a goal. This article aims to establish a debate on the contribution of agriculture to sustainable development and to analyze the role of the university in this process. Different alternatives to agricultural production that can contribute to sustainability are critically reviewed. These include good agricultural practices that seek to preserve the environment, including agroecology and organic agriculture, precision agriculture, and some applications of biotechnology. There is currently a need to establish evaluation methods that allow objective identification of progress or setbacks in the quest for sustainability. In the implementation of the concept of sustainable agriculture, universities have an essential role to play as the main source of scientific truth and center of knowledge, without detracting from the legitimacy and value of other epistemic systems such as peasant, traditional, or local knowledge.

Keywords

sustainability; farmer knowledge; environmental management; higher education; rural development

References

1. United Nations Development Programme (UNDP). Human Development Report 2002. Deepening Democracy in a Fragmented World. Oxford University Press; 2002.

2. Food and Agriculture Organization of the United Nations (FAO). Action Plan. Latin America and the Caribbean without Hunger Initiative (Spanish). Regional Office for Latin America and the Caribbean; 2007.

3. Leiva FR. Sustainability of agricultural systems (Spanish). Colombian Agronomy 1998; 15(2–3): 181–193.

4. Müller B. Montreal 2005. What Happened, and What It Means. Oxford Institute for Energy Studies; 2006.

5. Jehan S, Umana A. The environment-poverty nexus. Development Policy Journal 2003; 3(20): 53–70.

6. Organisation for Economic Co-operation and Development (OECD). Environmental Outlook. OECD; 2001.

7. Lal R. Carbon emission from farm operations. Environment International 2004; 30(7): 981–990. doi: 10.1016/j.envint.2004.03.005

8. Turner RK. Sustainability principles and practice. In: Sustainable Environmental Economics and Management Principles and Practice. Wiley; 1995. pp. 3–36.

9. Food and Agriculture Organization of the United Nations (FAO). Good Agricultural Practices (Spanish). FAO, Regional Office for Latin America and the Caribbean; 2004.

10. Altieri MA, Nicholls CI. Agroecology and the Search for a Truly Sustainable Agriculture. United Nations Environment Programme; 2005.

11. Kirchmann H, Thorvaldsson G. Challenging targets for future agriculture. European Journal of Agronomy 2000; 12(3–4): 145–161. doi: 10.1016/S1161-0301(99)00053-2

12. Pretty JN, Morison JIL, Hine RE. Reducing food poverty by increasing agricultural sustainability in developing countries. Agriculture, Ecosystems & Environment 2003; 95(1): 217–234. doi: 10.1016/S0167-8809(02)00087-7

13. Blackmore S. Precision farming: An introduction. Outlook on Agriculture 1994; 23(4): 275–280. doi: 10.1177/003072709402300407

14. Godwin RJ, Wood GA, Taylor JC, et al. Precision farming of cereal crops: A review of a six year experiment to develop management guidelines. Biosystems Engineering 2003; 84(4): 375–391. doi: 10.1016/S1537-5110(03)00031-X

15. Auernhammer H. Precision farming—The environmental challenge. Computers and Electronics in Agriculture 2001; 30(1–3): 31–43. doi: 10.1016/S0168-1699(00)00153-8

16. Leiva FR. Methodological approach to site-specific soil management for the sustainability and competitiveness of transitional crops in Colombia (Spanish). Suelos Ecuatoriales 2006; 36(2): 49–56.

17. Zhang N, Wang M, Wang N. Precision agriculture—A worldwide overview. Computers and Electronics in Agriculture 2002; 36(2–3): 113–132. doi: 10.1016/S0168-1699(02)00096-0

18. Leiva FR, Morris J, Blackmore SB. Precision farming techniques for sustainable agriculture. Papers presented at the First European Conference on Precision Agriculture; 7–10 September 1997; Coventry, UK.

19. König A, Cockburn A, Crevel RWR, et al. Assessment of the safety of foods derived from genetically modified (GM) crops. Food and Chemical Toxicology 2004; 42(7): 1047–1088. doi: 10.1016/j.fct.2004.02.019

20. Leiva FR, Villalobos RR. Systems Sustainability Assessment Cold Climate Agriculture: Case of Potato (Solanum Tuberosum) (Spanish). Unibiblos; 2007.

21. Harrington L, Jones PG, Winograd. Measurements and Indicators of Sustainability. CIAT; 1993.

22. Gallopín G. Sustainability and Sustainable Development (Spanish). United Nations; 2003.

Refbacks

There are currently no refbacks.

This work is licensed under a Creative Commons Attribution 4.0 International License.

This site is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Editor-in-Chief

Prof. Zhengjun Qiu

Zhejiang University, China

Honorary Editor-in-Chief

Cheng Sun

Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China

Processing Speed (2023)

- - - ＜7 days: submission to initial review decision;
  - - 56 days: received to online

News & Announcements

2024-09-11

Smart agriculture is becoming a reality!

Modern agricultural technology is evolving rapidly, with scientists collaborating with leading agricultural enterprises to develop intelligent management practices. These practices utilize advanced systems that provide tailored fertilization and treatment options for large-scale land management.

2024-05-01

Notice on the prohibition of using AI techniques to generate papers!

This journal values human initiative and intelligence, and the employment of AI technologies to write papers that replace the human mind is expressly prohibited. When there is a suspicious submission that uses AI tools to quickly piece together and generate research results, the editorial board of the journal will reject the article, and all journals under the publisher's umbrella will prohibit all authors from submitting their articles.

Readers and authors are asked to exercise caution and strictly adhere to the journal's policy regarding the usage of Artificial Intelligence Generated Content (AIGC) tools.

2024-01-14

We are recruiting Editorial Team Members

Advances in Modern Agriculture (AMA) is looking for members of the Editorial Board.

2023-12-28

Notice on the change of publication frequency

The publication frequency of Advances in Modern Agriculture is Quarterly since 2024, that means the journal will publish 4 issues per year from 2024 onwards.

More Announcements...

Journal Center

Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.

more

Article Tools

Indexing metadata

How to cite item

Review policy