id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
azojete-859	Yusuf, D. D.; Upahi, E. J. ; Yusuf, H. O.	Multi-Response Optimization of Planting Process of a Seed Ridge Planter using Response Surface Methodology (RSM)	2023	14	.pdf	application/pdf	5985	278	60	Conclusively, soil moisture availability is an important factor affecting the depth of seed planting in the study area 1.0 Introduction Planting is a crucial step for successful crop production (Finch-Savage, and Bassel, 2016). Seedling emergence (in 0.01ha) Depth of Planting (mm) Plant Spacing(mm) Number of Seeds Per Hill 1 1.32 16.74 x x x x 2 1.13 16.74 x x x x 3 0.94 12.37 x x x x 4 1.32 12.37 x x x x 5 1.32 21.10 x x x x 6 1.13 16.74 x x x x 7 1.13 21.10 x x x x 8 1.13 16.74 x x x x 9 0.94 16.74 x x x x 10 1.13 16.74 x x x x 11 1.13 16.74 x x x x 12 1.13 12.37 x x x x 13 0.94 21.10 x x x x x = values to be measured on field 2.2.5.1 Development of response surface empirical model for output responses The empirical model correlating output responses (seedling emergence, depth of seed planting, plant spacing, number of seeds per hole, energy consumed and number of damaged seeds - (y)) to the process factors (planting speed, moisture content) and the interaction between them is best explained below as generated by Design Expert 9.0 Software:: 𝑌𝑅𝑆𝑀 = 𝛾0 + 𝛾1𝑥1 + 𝛾2𝑥2 + 𝛾3𝑥1𝑥2 + 𝛾4𝑥1 2 + 𝛾5𝑥2 2 + 𝜖 (28) where, YRSM = Response Surface matrix output, 𝑥1 = planting speed (m/s), 𝑥2 = moisture content (% db), 𝑥1𝑥2 = interaction between planting speed and moisture content, 𝑥1 2 = square of the main effect (planting speed), 𝑥2 2 = square of the main effect (moisture content), 𝛾𝑖 = coefficients of the model parameters and ϵ = error value.	cache/azojete-859.pdf	txt/azojete-859.txt