Impact of Nutritional Management on Available Mineral Nitrogen and Soil Quality Properties in Coffee Agroecosystems

Olaya, Juan Fernando Casanova; Salcedo, Judith Rodríguez; Ordoñez, María-Cristina

doi:10.3390/agriculture9120260

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…The soil organic carbon measurements in this study align with those reported by Kurniawan et al (2021), that the application of organic fertilizer alone or in combination with NPK fertilizer can increase soil organic carbon. The results of Olaya et al (2019) also showed that organic fertilizers sourced from manure have the potential to boost the buildup of soil organic carbon, resulting in alterations in the microbial community structure and promotion of microbial biomass.…”

Section: Soil Organic Carbon Mattermentioning

confidence: 96%

The Effect of Coffee Canopy Pruning and Fertilization on Coffee Growth and Soil Physical Properties

Rohani,

Prayogo,

Suprayogo

et al. 2024

J. appl. agricultural sci. technol.

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Arabica coffee is primarily cultivated in agroforestry systems in Indonesia, but limited local knowledge and technology adoption hinder its productivity due to insufficient practices in coffee pruning management. This study aims to analyze variations in coffee canopy pruning (Pruning+Bending) management and the impact on plant growth and soil physical characteristics.. The experiment employed a split-plot experimental design and utilized the Fisher test (5%) to assess the treatment effects. The primary plot focused on coffee canopy pruning using two management options: (1) Pruning (PR) and (2) Bending (BN). The subplots included various types and doses of fertilizer treatments: (1) Control (F0), (2) Chicken manure (F1), (3) Chicken manure+NPK fertilizer (F2), and (4) NPK fertilizer (F3). Each experimental plot covered an area of 20x20 m and contained 50 coffee plants. Bending techniques represent alternative pruning methods, and, in general, they have a significant impact on improving several coffee parameters compared to total pruning. Regarding the soil’s physical properties, the bending technique exhibited a higher infiltration rate than pruning. The management approach of Bending+Chicken manure: NPK fertilizer (BNF2) enhanced various coffee parameters, resulting in an increased stem diameter of 4.79 cm, new shoot length of 471.20 cm, and chlorophyll content of 6.83 mg/g. Furthermore, this treatment increased soil organic carbon content by 7.51% and reduced bulk density to 0.58 g/cm. In conclusion, the bending technique wasproven to be more advantageous than pruning, especially when combined with chicken manure and NPK fertilizer for enhancing coffee management among farmers.

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Section: Soil Organic Carbon Mattermentioning

confidence: 96%

The Effect of Coffee Canopy Pruning and Fertilization on Coffee Growth and Soil Physical Properties

Rohani,

Prayogo,

Suprayogo

et al. 2024

J. appl. agricultural sci. technol.

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Multi-Feature Optimization Study of Soil Total Nitrogen Content Detection Based on Thermal Cracking and Artificial Olfactory System

et al. 2021

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To improve the accuracy of detecting soil total nitrogen (STN) content by an artificial olfactory system, this paper proposes a multi-feature optimization method for soil total nitrogen content based on an artificial olfactory system. Ten different metal–oxide semiconductor gas sensors were selected to form a sensor array to collect soil gas and generate response curves. Additionally, six features such as the response area, maximum value, average differential coefficient, standard deviation value, average value, and 15th-second transient value of each sensor response curve were extracted to construct an artificial olfactory feature space (10×6). Moreover, the relationship between feature space and soil total nitrogen content was used to establish backpropagation neural network (BPNN), extreme learning machine (ELM), and partial least squares regression (PLSR) models were used, and the coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) were selected as prediction performance indicators. The Monte Carlo cross-validation (MCCV) and K-means improved leave-one-out cross-validation (K-means LOOCV) were adopted to identify and remove abnormal samples in the feature space and establish the BPNN model, respectively. There were significant improvements before and after comparing the two rejection methods, among which the MCCV rejection method was superior, where values for R2, RMSE, and RPD were 0.75671, 0.33517, and 1.7938, respectively. After removing the abnormal samples, the soil samples were then subjected to feature-optimized dimensionality reduction using principal component analysis (PCA) and genetic algorithm-based optimization backpropagation neural network (GA-BP). The test results showed that after feature optimization the model indicators performed better than those of the unoptimized model, and the PLSR model with GA-BP for feature optimization had the best prediction effect, with an R2 value of 0.93848, RPD value of 3.5666, and RMSE value of 0.16857 in the test set. R2 and RPD values improved by 14.01% and 50.60%, respectively, compared with those before optimization, and RMSE value decreased by 45.16%, which effectively improved the accuracy of the artificial olfactory system in detecting soil total nitrogen content and could achieve more accurate quantitative prediction of soil total nitrogen content.

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Land-Use Change and Management Intensification Is Associated with Shifts in Composition of Soil Microbial Communities and Their Functional Diversity in Coffee Agroecosystems

et al. 2022

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Despite the central role of microorganisms in soil fertility, little understanding exists regarding the impact of management practices and soil microbial diversity on soil processes. Strong correlations among soil microbial composition, management practices, and microbially mediated processes have been previously shown. However, limited integration of the different parameters has hindered our understanding of agroecosystem functioning. Multivariate analyses of these systems allow simultaneous evaluation of the parameters and can lead to hypotheses on the microbial groups involved in specific nutrient transformations. In the present study, using a multivariate approach, we investigated the effect of microbial composition (16SrDNA sequencing) and soil properties in carbon mineralization (CMIN) (BIOLOG™, Hayward, CA, USA) across different management categories on coffee agroecosystems in Mexico. Results showed that (i) changes in soil physicochemical variables were related to management, not to region, (ii) microbial composition was associated with changes in management intensity, (iii) specific bacterial groups were associated with different management categories, and (iv) there was a broader utilization range of carbon sources in non-managed plots. The identification of specific bacterial groups, management practices, and soil parameters, and their correlation with the utilization range of carbon sources, presents the possibility to experimentally test hypotheses on the interplay of all these components and further our understanding of agroecosystem functioning and sustainable management.

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Impact of Nutritional Management on Available Mineral Nitrogen and Soil Quality Properties in Coffee Agroecosystems

Cited by 4 publications

References 24 publications

The Effect of Coffee Canopy Pruning and Fertilization on Coffee Growth and Soil Physical Properties

The Effect of Coffee Canopy Pruning and Fertilization on Coffee Growth and Soil Physical Properties

Multi-Feature Optimization Study of Soil Total Nitrogen Content Detection Based on Thermal Cracking and Artificial Olfactory System

Land-Use Change and Management Intensification Is Associated with Shifts in Composition of Soil Microbial Communities and Their Functional Diversity in Coffee Agroecosystems

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