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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.

Agroecology or modern conventional agriculture? Positions of Argentine rural extensionists

F Landini, Maite Regina Beramendi

Article ID: 1985
Vol 1, Issue 1, 2020

DOI: https://doi.org/10.54517/ama.v1i1.1985
VIEWS - 1969 (Abstract)

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Abstract

At a global level, there is a growing concern about the sustainability of agricultural production models. In particular, the impacts and environmental passives derived from the implementation of the principles of the so-called Green Revolution. In contrast, agroecology gains supporters in the context of a different productive model, though limited due to lower levels of agricultural productivity. Nevertheless, for any of these models to come into practice, it has to go through farmers’ decision-making. Thus, acknowledging the key role played by rural extensionists in farmers’ decision-making process, in this article the extensionists’ personal positioning and that of their institutions are analyzed in the context of the contrast between agroecology and conventional agriculture. A total of 583 rural extensionists who work in the National Institute of Agricultural Technology, the Subsecretariat of Family Farming and Territorial Development, and other Argentine institutions replied to an online questionnaire. It included sociodemographic questions and asked to position oneself and one’s institution using a five-level Likert-type item, in which agroecology and conventional agriculture were the poles. Results show that, on average, extensionists have a tendency towards the agroecological pole, while they situate their institutions halfway between both agricultural models. In general, women, who have no university degree, and who hold a degree in social sciences, are more oriented to the agroecological pole.

Keywords

organic agriculture; perceptions; pesticides; sustainability

References

1. da Silva EM, Vieira ETV, Tashima LCN, Guilherme DO. A sustainability rereading of agrarian production systems (Portuguese). Interações (Campo Grande) 2017; 18(4): 43–54. doi: 10.20435/inter.v18i4.1527

2. Betancur LM, Girón SM, Betancur LF. The milpa as an alternative for agroecological conversion of conventional agricultural systems of beans (Phaseolus vulgaris), in the municipality El Carmen de Viboral, Colombia (Spanish). IDESIA 2018; 36(1): 123131.

3. Capellesso AJ, Cazella AA. Agroecosystem sustainability indicator: case study in corn growing areas (Portuguese). Ciência Rural 2013; 43(12): 2297–2303. doi: 10.1590/s0103-84782013005000130

4. Kavitha V, Chandran K. Organic farming in conserving bio diversity in India—A review. Agricultural Reviews 2017; 38(04). doi: 10.18805/ag.r-1709

5. Kovács‐Hostyánszki A, Espíndola A, Vanbergen AJ, et al. Ecological intensification to mitigate impacts of conventional intensive land use on pollinators and pollination. Ecology Letters 2017; 20(5): 673–689. doi: 10.1111/ele.12762

6. Vasquez P, Vignolles M. Organic agroproductive establishment vs. conventional agriculture: Tandil district, province of Buenos Aires (Spanish). Sociedade & Natureza 2015; 27(2): 267–280. doi: 10.1590/1982-451320150206

7. Blesh JM, Barrett GW. Farmers’ attitudes regarding agrolandscape ecology: A regional comparison. Journal of Sustainable Agriculture 2006; 28(3): 121–143. doi: 10.1300/J064v28n03_10

8. Capellesso AJ, Cazella AA, Schmitt Filho AL, et al. Economic and environmental impacts of production intensification in agriculture: comparing transgenic, conventional, and agroecological maize crops. Agroecology and Sustainable Food Systems 2016; 40(3): 215–236. doi: 10.1080/21683565.2015.1128508

9. Paleologos MF, Iermanó MJ, Blandi ML, Sarandón SJ. Ecological relationships: a central aspect in the redesign of sustainable agroecosystems, from Agroecology. REDES: Revista do Desenvolvimento Regional 2017; 22(2): 92–115. doi: 10.17058/redes.v22i2.9346

10. Sarandón SJ, Marasas ME. Brief history of agroecology in Argentina: Origins, evolution and future perspectives. Agroecología 2015; 10(2): 93–102.

11. Landini F, Beramendi M, Vargas G. Use and management of agrochemicals in family farmers and rural workers in five Argentine provinces. Revista Argentina de Salud Pública 2019; 10(38): 22–28.

12. Pimbert M. Agroecology as an alternative vision to conventional development and climate-smart agriculture. Development 2015; 58(2–3): 286–298. doi: 10.1057/s41301-016-0013-5

13. Altieri M, Nicholls C. Agroecology: Theory and practice for a sustainable agriculture (No. 630.2745 A468ag). Available online: http://www.agro.unc.edu.ar/~biblio/AGROECOLOGIA2%5B1%5D.pdf (accessed on 31 October 2018).

14. Shennan C, Krupnik TJ, Baird G, et al. Organic and conventional agriculture: A useful framing? Annual Review of Environment and Resources 2017; 42(1): 317–346. doi: 10.1146/annurev-environ-110615-085750

15. Zhao R, He P, Xie J, et al. Ecological intensification management of maize in northeast China: Agronomic and environmental response. Agriculture, Ecosystems & Environment 2016; 224: 123–130. doi: 10.1016/j.agee.2016.03.038

16. Palmisano T. Alternative agriculture in the context of agribusiness. Experiences in the province of Buenos Aires, Argentina (Spanish). Social Studies Magazine of Contemporary Food and Regional Development 2018; 28(51). doi: 10.24836/es.v28i51.513

17. Landini F. Peasant economic rationality. Agrarian World 2011; 12(23).

18. Kamiyama A, Maria IC, Souza DCC, et al. Environmental perception of producers and quality only in organic and conventional properties (Spanish). Bragantia 2011; 70(1): 176–184. doi: 10.1590/s0006-87052011000100024

19. Chalak A, Irani A, Chaaban J, et al. Farmers’ willingness to adopt conservation agriculture: New evidence from Lebanon. Environmental Management 2017; 60(4): 693–704. doi: 10.1007/s00267-017-0904-6

20. Stotz EN. The limits of conventional agriculture and the reasons for its persistence: Case study of Sumidouro, RJ (Spanish). Revista Brasileira de Saúde Ocupacional 2012; 37(125): 114–126. doi: 10.1590/s0303-76572012000100014

21. Christoplos I. Mobilizing the potential of rural and agricultural extension. Available online: https://agris.fao.org/search/en/providers/122621/records/64724fc753aa8c896305dcf4 (accessed on 31 October 2018).

22. Ingram J. Agronomist–farmer knowledge encounters: An analysis of knowledge exchange in the context of best management practices in England. Agriculture and Human Values 2008; 25(3): 405–418. doi: 10.1007/s10460-008-9134-0

23. Salomonsson L, Nilsson A, Palmer S, et al. Farming systems education: Case study of Swedish test pilots. Renewable Agriculture and Food Systems 2008; 24(1): 48–59. doi: 10.1017/s1742170508002408

24. Guevara MA, Wesz VJ Jr. Gender and agroecology: Case studies in Brazil (Spanish). Agroecología 2013; 7(2): 101–110.

25. Siliprandi E. Women and agroecology. New political subjects in family farming. Investigaciones Feministas 2010; 1: 125–137.

26. DA Ros CA. The contribution of field visits to the teaching of agricultural sciences at UFRRJ (Portuguese). Revista Ciência em Extensão 2012; 8(1): 107–122.

27. Olivera Méndez A. Environmental psychology and rurality. Available online: http://biblioteca.clacso.edu.ar/clacso/se/20150213020711/Hacia_psicologia_ rural.pdf (accessed on 31 October 2018).

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