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Agronomy
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Crop Management and Productivity by Remote Sensing for Sustainable Agricultural...
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Crop Management and Productivity by Remote Sensing for Sustainable Agricultural Systems

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (30 January 2024) | Viewed by 7101

Special Issue Editor

Dr. Alberto San Bautista

E-Mail Website
Guest Editor
Crop Production Department, Universitat Politècnica de València, Cno Vera 14, 46020 Valencia, Spain
Interests: agronomy and remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increase in the world’s population and the future scenarios derived from climate change mark the challenges for society: sustainable development and food security. Agriculture constitutes a determining element in economic and social development, although it is necessary to improve the efficient use of natural resources and production inputs, reducing the impact on the environment. This objective will be achieved to a greater extent by increasing the productivity of agricultural systems, according to FAO estimates.

An adequate interaction between the components of an agricultural system (climate, topography, vegetation and other organisms, soil, and natural resources) is fundamental to determine its productivity and sustainability. In this context, real-time phenology monitoring is necessary to react to changing conditions and reduce the impact on crop production. One way to reduce food production shortages and ensure food security is the use of crop yield prediction and crop management monitoring.

A tool capable of helping to achieve this is remote sensing applied to agronomy, since the information provided in a non-destructive and systematic way allows the spatial and temporal characterization of the main properties of agricultural systems.

Dr. Alberto San Bautista
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • crop management
  • productivity
  • sustainable
  • efficiency

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Related Special Issue

Published Papers (3 papers)

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Research

11 pages, 985 KiB  
Article
Utilizing Remote Sensing to Quantify the Performance of Soybean Insecticide Seed Treatments
by Jeffrey M. Hegstad, Hua Mo, Adam P. Gaspar and Dwain Rule
Agronomy 2024, 14(2), 340; https://doi.org/10.3390/agronomy14020340 - 7 Feb 2024
Cited by 1 | Viewed by 1300
Abstract
Soybean (Glycine max) is one of the most important oilseed crops grown in North America and a key contributor to the global protein supply. Insect feeding by a major soybean pest, the bean leaf beetle (BLB; Cerotoma trifurcata), can result [...] Read more.
Soybean (Glycine max) is one of the most important oilseed crops grown in North America and a key contributor to the global protein supply. Insect feeding by a major soybean pest, the bean leaf beetle (BLB; Cerotoma trifurcata), can result in economic yield loss if not controlled. The objective of this research was to use unmanned aerial vehicle (UAV) image analysis to compare the agronomic and efficacy traits of two soybean insecticide seed treatments (IST) in locations with BLB feeding. Across the 2018–2023 field trial locations, 29 had low BLB feeding pressure (less than 25% feeding damage to no IST plots) and 31 had high BLB feeding pressure (greater than 25% feeding damage to no IST plots). In low BLB pressure locations, cyantraniliprole and imidacloprid seed treatments had significantly higher BLB efficacy, significantly higher UAV greenness, and significantly higher final yield as compared to no IST. In high BLB pressure locations, cyantraniliprole and imidacloprid seed treatments were significantly better compared to no IST for BLB efficacy, UAV emergence, UAV vigor, UAV greenness, and final yield. In high BLB pressure locations, cyantraniliprole had significantly higher BLB efficacy, significantly better UAV emergence, and significantly higher yield compared to imidacloprid. The cyantraniliprole treatment had a +254.5 kg/ha increase compared to no IST in low BLB pressure locations and a +213.7 kg/ha increase in high BLB pressure locations. The imidacloprid treatment had a +163.4 kg/ha yield increase compared to no IST in low BLB pressure locations and a +121.4 kg/ha yield increase in high BLB pressure locations. The use of UAV image analysis enabled quantification of the effect of BLB feeding on early-season agronomic traits and, when combined with efficacy and final yield data, successfully differentiated the performance of two soybean ISTs in environments with low or high insect pressure. Full article
(This article belongs to the Special Issue Crop Management and Productivity by Remote Sensing for Sustainable Agricultural Systems)
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20 pages, 5356 KiB  
Article
Evaluating the Efficacy of Sentinel-2B and Landsat-8 for Estimating and Mapping Wheat Straw Cover in Rice–Wheat Fields
by Muhammad Sohail Memon, Shuren Chen, Yaxiao Niu, Weiwei Zhou, Osama Elsherbiny, Runzhi Liang, Zhiqiang Du and Xiaohu Guo
Agronomy 2023, 13(11), 2691; https://doi.org/10.3390/agronomy13112691 - 26 Oct 2023
Cited by 6 | Viewed by 2116
Abstract
Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface [...] Read more.
Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface is crucial for monitoring tillage intensity and crop yield performance. Thus, a novel research study is conducted to develop an innovative approach for accurately estimating and mapping the Wheat Straw Cover (WSC) percentage through two different multispectral satellites (Sentinel-2B MSI and Landsat-8 OLI-TIRS), using remote sensing-based techniques in Changshu County, China. The field measurements were collected from 80 distinct sites and eight images were acquired through both satellites for the analysis process by applying Crop Residue Indices (CRIs). The results indicate that the coefficients of determination (R2) of the Normalized Difference Tillage Index (NDTI) computed by Sentinel-2 and Landsat-8 were 0.80 and 0.70, respectively, and the root-mean-square deviation (RMSD) values were in the range from 6.88 to 12.04% for CRIs for both satellite data. Additionally, the comparative analysis of the developed model revealed that NDTI was R2 = 0.85 and R2 = 0.77, followed by STI, R2 = 0.82 and R2 = 0.80 and NDRI, R2 = 0.69 and R2 = 0.56 for Sentinel-2B and Landsat-8 data, respectively. Hence, the correlation strength of NDTI, STI and NDRI with WSC percentages was markedly superior by using Sentinel-2B spectral data compared to Landsat-8 ones. Moreover, the NDTI of Sentinel-2B data was the most accurate in mapping the WSC percentage in four categories, with an overall accuracy of 86.53% (κ = 0.78), surpassing the other CRI indices. Therefore, these findings suggest that the multispectral imagery of Sentinel-2B bolstered with enhanced temporal and spatial data was superior for precisely estimating and mapping the WSC percentage compared to Landsat-8 data over a large-scale agricultural region. Full article
(This article belongs to the Special Issue Crop Management and Productivity by Remote Sensing for Sustainable Agricultural Systems)
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21 pages, 8513 KiB  
Article
Integrating SAR Sentinel-1A and DSSAT CROPGRO Simulation Model for Peanut Yield Gap Analysis
by Subramanian Thirumeninathan, Sellaperumal Pazhanivelan, N. S. Sudarmanian, Kaliaperumal Ragunath, Ramalingam Kumaraperumal, Govindasamy Srinivasan and Ramalingam Mohan
Agronomy 2023, 13(3), 889; https://doi.org/10.3390/agronomy13030889 - 17 Mar 2023
Cited by 3 | Viewed by 2950
Abstract
Crop yield data are critical for managing agricultural sustainability and assessing national food security. This study aims at increasing peanut productivity from its current levels by analyzing the yield gap (difference) of potential production between theoretical yield and actual farmers’ yields. The spatial [...] Read more.
Crop yield data are critical for managing agricultural sustainability and assessing national food security. This study aims at increasing peanut productivity from its current levels by analyzing the yield gap (difference) of potential production between theoretical yield and actual farmers’ yields. The spatial yield gap of peanut for the Tiruvannamalai district of Tamil Nadu is examined in this investigation by integrating the products of microwave remote sensing (SAR Sentinel-1A) with the DSSAT CROPGRO Peanut simulation model. The CROPGRO (crop growth) Peanut model was calibrated and validated by conducting a field experiment at Oilseeds Research Station, Tindivanam during Rabi (spring) 2019 for predominant cultivars, i.e., TMV 7, TMV 13, VRI 2 and G 7. Actual attainable yield was recorded by organizing crop cutting experiments (CCEs) with the help of the Department of Agriculture Economics and Statistics in the respective monitoring villages. The regression analysis between the maximum recorded DSSAT leaf area index (LAI) at the peak flowering stage of peanut and the yield recorded by CCEs for the spatial yield estimation of peanut in the Tiruvannamalai district of Tamil Nadu during Rabi 2021 was carried out using ArcGIS 10.6 software. The DSSAT CROPGRO simulated potential yield ranged from 3194 to 4843 kg/ha, whereas actual yield ranged from 1228 to 3106 kg/ha, with a considerable disparity between the actual and potential yield levels (from 1217 to 2346 kg/ha) of the monitored locations. The minimum, maximum and average yield gaps in peanut for Tiruvannamalai district were assessed as 1890, 2324 and 2134 kg/ha, respectively. In order to reduce the production difference of peanut cultivation, farmers should focus more on management issues such as time of sowing, irrigation or water management, quantity and sources of nutrients, cultivar selection and availability of quality seeds tailored to each region. Full article
(This article belongs to the Special Issue Crop Management and Productivity by Remote Sensing for Sustainable Agricultural Systems)
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