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Forecasting maize yield from growth parameters using machine learning in a biochar-inorganic fertilizer amended soil under drip irrigation
(2025-12-01)
Faloye, Oluwaseun Temitope
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Ajayi, Ayodele Ebenezer
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Kamchoom, Viroon
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Sinsamutpadung, Natdanai
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Adeyeri, Oluwafemi
The combined application of biochar and inorganic fertilizers has demonstrated significant potential to enhance crop productivity under both rainfed and irrigated conditions. However, predictive modeling approaches utilizing machine learning (ML) to simulate field outcomes under diverse agronomic scenarios remain understudied. This study addresses two critical objectives: (i) to evaluate the efficacy of ML models—Support Vector Machine (SVM), Artificial Neural Network (ANN), and Boosted Trees (BT)—in predicting maize grain yieldin biochar-inorganic fertizer amended soil under drip irrigation; and (ii) to identify the growth stage(s) and ML models that deliver the most accurate predictions. A three-year factorial field experiment was conducted during dry seasons, testing five biochar rates (0, 3, 6, 10 and 20 t/ha), two fertilizer levels (0 and 300 kg/ha), and deficit irrigation treatments (60%, 80%, and 100% of full irrigation). Growth parameters were measured at vegetative (35 days after planting – DAP), flowering stage (49 DAP), and maturity stage (77 DAP), with grain yield recorded at harvest (90 DAP). The measured growth parameters at the different DAP were used for the grain yield forecast. 70, 15 and 15% of the dataset were used for model training, validation and testing, respectively. Field results revealed progressive increases in growth parameters from vegetative to maturity stages, with treatment efficacy following the order: control < biochar-only < fertilizer-only < combined biochar-fertilizer. ML predictions mirrored this hierarchy, with ANN achieving superior accuracy (R² = 0.73–0.85, RMSE = 0.43–0.76, NRMSE = 0.095–0.17 at maturity) compared to SVM and BT. Predictive performance was weakest at the vegetative stage (35 DAP) but improved during flowering (49 DAP) and maturity (77 DAP), underscoring the importance of later growth data for reliable yield forecasting. This study demonstrates that ML models, particularly ANN, can effectively predict maize yield using accessible growth metrics, offering a cost- and labor-efficient complement to traditional field research. By enabling rapid scenario analysis, such models empower stakeholders to optimize resource allocation and inform crop management decisions under varying irrigation and soil amendment strategies.
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Comparison of cost and return between double crop with one crop per year of Maize Community Enterprise in Hin Sorn Subdistrict, Saraburi Province
(2024-05-01)
Khermkhan, J.
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Polyorach, S.
The Hin Son Sub-district Maize Community Enterprise is employed two distinct planting systems of double cropping and single-crop cultivation per year. The findings revealed that under the single-crop approach, the average production cost amounted to 3,630 baht per rai, yielding returns of 7,400 baht per rai annually, resulting in a profit of 3,770 baht per rai per year. In contrast, the double crop method incurred costs of 3,130 baht per rai, with yields reached 19,240 baht per rai. Consequently, this approach is generated profits of 12,980 baht per rai annually, or 6,490 baht per rai per crop for each planting cycle.
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Residual effects of intercrop system and rice as mono crop on growth and yield of maize in rice-based production system
(2020-01-01)
Nattawut, S.
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Lampan, K.
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Sukunya, Y.
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Sudteerak, S.
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Patcharaporn, S.
The plot previously planted with maize, peanut and sweet potato as intercropping system had higher organic matter, available phosphorus, exchangeable potassium, exchangeable magnesium extractable iron, manganese, zinc and copper, than the plot previously planted with rice as mono crop. The results suggested that intercropping system improved soil quality and might be beneficial to succeeding crops. Maize and rice grown after intercropping system were not significantly different for growth parameters, yield and yield components. Maize grown after intercropping system was better than its grown after rice because of higher yield (7,895 kg/ha and 8,558 kg/ha) for the plot after rice harvest and the plot after intercropping system. The results indicated that maize cultivation could be decreased in rice cultivation in irrigated areas of the dry season. It is possible the rice growers can select both systems.
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Upland rice vinegar vapor inhibits spore germination, hyphal growth and aflatoxin formation in Aspergillus flavus on maize grains
(2017-01-01)
Pornpukdeewattana, Soisuda
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Kerdpiboon, Soraya
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Jindaprasert, Aphacha
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Pandee, Patcharaporn
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Teerarak, Montinee
The efficacy of vapor-phase (VP) upland rice vinegar (URV) was investigated as a bio-fumigant for maize, to reduce consumer health risks associated with spore and toxin formation by Aspergillus flavus. Complete reduction of mycelial growth occurred with in vitro VP exposure to URV (containing 0.0017 mmol/L acetic acid) or with VP exposure to pure acetic acid (PAA) (containing 0.0023 mmol/L acetic acid). No significant differences were observed between the two materials after 90 min exposures. Using gas chromatography-mass spectrometry (GC-MS), URV vapor was shown to contain volatiles having antifungal activities. These are identified as isoamylalcohol, 1-butanol, 3-methyl-, acetate and β-phenylethyl acetate. It is suggested these volatiles increase the antifungal effectiveness of URV. Exposure to VP-URV (containing 0.0043 mmol/L AA) for 5 h completely eliminated viable spores of A. flavus on maize seeds (23% moisture content) previously inoculated with 4.43 ± 0.28 log spores/g). At the same time, aflatoxin production decreased, as VP-URV exposure increased. Hence, VP-URV is shown to be an effective control agent for A. flavus mycelial growth and aflatoxin formation on maize, so effectively reducing the potential for consumer health risks due to this widespread fungus.
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Effects of Aglaia odorata granules on the seedling growth of major maize weeds and the influence of soil type on the granule residue's efficacy
(2012-09-01)
Laosinwattana, Chamroon
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Teerarak, Montinee
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Charoenying, Patchanee
This study was undertaken to explore the potential of Aglaia odorata Lour. granules in various soil types (sand, sandy loam and clay) at 0.25, 0.5 and 1taiha-1 to inhibit two major maize weeds, Digitaria adscendens and Trianthema portulacastrum L., in an experimental greenhouse. The results indicated that the emergence and seedling growth of both weeds were inhibited but varied with the soil type: the inhibitory effect of the A. odorata granules was stronger when applied in sand, followed by sandy loam and clay. The weed species responded differently to the A. odorata granule application. Generally, the inhibitory effect was greater for D. adscendens than for T. portulacastrum. Additionally, the potential of the A. odorata granules for weed control in maize field plots was evaluated. The three major weeds that were found in the experimental field were D. adscendens, T. portulacastrum and Amaranthus gracilis. The highest A. odorata granule dose (1taiha-1) had a significant inhibitory effect on the emergence and growth of all of the weed species and their emergence was reduced by 96.7%, 47.2% and 56.2%, respectively, compared with the control treatment (unweeded). The dry weight of the weeds was reduced in a similar manner to their emergence. In contrast, the emergence of maize was not affected by any of the A. odorata granule applications. Rather, at an application rate of 1taiha-1, the maize yield increased significantly and the effect was similar to that observed with an atrazine herbicide application under field conditions. © 2012 Weed Science Society of Japan.

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