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Zahlul Ikhsan
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zahlul.chan@gmail.com
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+6285271067099
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agrivolution.cibn@gmail.com
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Jalan delima 8 Perumnas Belimbing, Kelurahan Kuranji, Kec Kuranji, Padang, Sumatra Barat
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Agricultural Revolution Journal
Published by Cendekiawan Inovatif dan Berkelanjutan Nusantara
ISSN : -     EISSN : 31089062     DOI : https://doi.org/10.64570/agrivolution.v1i1.21
Core Subject : Science, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Materials Science & Nanotechnology
Aim: Agrivolution is a peer-reviewed, open-access journal committed to advancing technological innovation in agriculture to enhance productivity, efficiency, and sustainability. The journal provides a cutting-edge platform for scientists, engineers, and agritech innovators to explore the integration of artificial intelligence, robotics, automation, and biotechnological advancements in modern farming systems. With a focus on precision agriculture, controlled environment farming, and next-generation agricultural machinery, Agrivolution fosters interdisciplinary research that bridges engineering, computational science, and biological sciences. We aim to accelerate the development of innovative, data-driven, and climate-resilient agricultural solutions to address global food security challenges. Scope: Smart Farming & Precision Agriculture Artificial Intelligence, Machine Learning & IoT in Agriculture Biotechnological Innovations for Sustainable Crop Production Soil Health Management & Regenerative Agricultural Techniques Controlled Environment Agriculture (CEA) & Vertical Farming
Arjuna Subject : Ilmu Pertanian dan Biologi (Semua) - Agronomi dan Ilmu Tanaman
Articles 13 Documents
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Dynamics of Phosphorus Sorption and Desorption in Ultisols Ameliorated with Humic Substances from Potential Ameliorants Amsar Maulana; Dewi Rezki; Irwan Darfis; Zahlul Ikhsan; Herviyanti Herviyanti
Agricultural Revolution Journal Vol. 1 No. 2 (2025): Agricultural Revolution Journal
Publisher : CIB Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64570/agrivolution.v1i2.32

Abstract

Ultisols have a very high P sorption capacity, which limits the availability of P to plants. Therefore, it is necessary to understand the P sorption-desorption mechanism after humic substances (HS) amelioration to improve fertilizer efficiency and land productivity. This study has examined the complexity of phosphorus fixation and the potential of HS in modifying the surface charge of Ultisols. This study used the batch equilibrium method and the Freundlich and Langmuir isotherm model approaches. Meanwhile, the surface charge characteristics of Ultisols, amended with humic substances (HS) from various potential ameliorants (control, HS-chicken manure, HS-black soldier fly, HS-wet decanter solid, and HS-peat), were evaluated using a completely randomized design with three replications. The HS from potential ameliorants significantly increases pH, PZC, electrical conductivity (EC), mineral and organic matter composition, CEC, and reduces potential redox (Eh), thereby increasing the soil's negative charge and buffering capacity. The HS—wet decanter solid and chicken manure were most effective, as they were proven to remove Al-exchange to unmeasurable levels through strong complexation between Al³⁺ and carboxylate and phenolic groups. The HS—wet decanter solid and chicken manure also drastically reduce P sorption and increase desorption through ligand competition and blocking of Al/Fe reactive sites, which resulted in increased P availability up to >600 mg kg-1 P2O5 at a concentration of 1000 mg L-1 P or 2290 mg kg⁻¹ P₂O₅ or 6.37g SP-36 per liter or 6.37 kg SP-36 per hectare for an application volume of 1,000 liters per hectare.
Performance Evaluation of ARIMA and ANN Models for Forecasting Oil Palm Production Trends Hermiza Mardesci; Dita Fitriani
Agricultural Revolution Journal Vol. 1 No. 2 (2025): Agricultural Revolution Journal
Publisher : CIB Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64570/agrivolution.v1i2.33

Abstract

This study compares the performance of the Autoregressive Integrated Moving Average (ARIMA) model and an Artificial Neural Network (ANN) in forecasting annual palm oil production in Kampar Regency, using a univariate time series covering the period from 2013 to 2024. The forecasting aim is to support regional agricultural planning and decision-making in one of Riau Province’s key oil palm-producing regions. The ARIMA model was developed using the Box–Jenkins approach, which involves stationarity testing, optimal model identification, parameter estimation, and residual diagnostics, including ACF/PACF, Shapiro–Wilk, Jarque–Bera, and Ljung–Box tests. A feedforward ANN with three lagged inputs, five hidden neurons, sigmoid activation, and backpropagation training was constructed for comparison. Model performance was evaluated using RMSE, MAPE, and R². The results indicate that the ARIMA (1,1,1) model yields more stable and reliable forecasts, with diagnostic tests confirming white noise residuals and no significant autocorrelation. Conversely, the ANN model produced higher errors and indications of overfitting, likely due to the limited number of observations and the sharp increase in production recorded in the final data year. While ANN captured a stronger upward trend, which may represent an optimistic scenario, ARIMA provided more conservative and statistically valid forecasts under constrained data conditions. Overall, the ARIMA(1,1,1) model proved more suitable for the short univariate palm oil production series, yielding lower forecasting errors (RMSE = 273.88; MAPE = 8.92%) than the ANN model (RMSE = 283.53; MAPE = 9.03%).
The Effect of Soaking Red Chili Seeds (Capsicum annum L.) with Various Trichoderma Species in Suppressing Chili Seed-Borne Pathogens Caused by Colletotrichum Rani Selpia Siregar; Dini Puspita Yanty; Jumaria Nasution
Agricultural Revolution Journal Vol. 1 No. 2 (2025): Agricultural Revolution Journal
Publisher : CIB Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64570/agrivolution.v1i2.38

Abstract

Red chili (Capsicum annuum L.) is a horticultural plant that belongs to the Solanaceae family. Chili cultivation often encounters problems, namely anthracnose disease caused by the fungus Colletotrichum. This study aims to determine the most effective Trichoderma species for suppressing red chili seed-borne Colletotrichum pathogens. This study was conducted from March to April 2025. This research method used a Completely Randomized Design (CRD) with 5 treatments and 3 replications. P0 = Control (Without soaking with Trichoderma suspension) P1 = Red chili seeds soaked with Trichoderma harzianum suspension for 9 hours P2 = Red chili seeds soaked with Trichoderma viride suspension for 9 hours P3 = Red chili seeds soaked with Trichoderma asperellum suspension for 9 hours P4 = Red chili seeds soaked with fungicide for 5 minutes. The parameters observed were: Germination percentage (%), seedling height (cm), and number of leaves (stalks). Trichoderma treatment significantly affected germination percentage, red chili seedling height, and leaf number. In terms of treatment percentage, P1, P2, and P3 were significantly different in red chili seed germination percentage, but the three treatments were not significantly different from P0 and P4. Soaking local chili seeds with various Trichoderma suspensions was best found in the types of Trichoderma viride and Trichoderma asperellum because it can be seen from the value of the results of the percentage of germination power, the average height of seedlings, and the number of leaves, the values ​​​​of Trichoderma viride and Trichoderma asperellum were the highest.

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