International Journal of Environment and Climate Change

Ginger (Zingiber officinale) is a herbaceous perennial plant widely recognized as a popular spice across the world. India ranks as the largest producer and consumer of ginger globally. However, ginger cultivation faces several production constraints, primarily due to variability in weather conditions. The uncertainty associated with weather patterns poses significant challenges for both farmers and policymakers, hindering timely decision-making at the field and market levels. Crop weather models serve as a reliable statistical tool that represents complex relationship between crop and weather parameters. A field experiment conducted at Kerala Agricultural University, Vellanikkara, Thrissur district using Maran variety in 2021-2022. The experiment was conducted using a split plot design. The main plot treatments consisted of four planting dates: 1st June (D1), 15th June (D2), 1st July (D3), and 15th July (D4). The subplot treatments comprised three types of organic mulches: green leaves (M1), paddy straw (M2), and dry coconut leaves (M3). The June 1^st planted crop yielded more fresh ginger (19957.81 kg ha^-1) than the July 15^th planted crop. A consistent decline in observed yield was noted from D1 to D4 across all mulch types. Early planting (D1) showed the highest yields, (15,276.74 kg ha⁻¹), whereas the latest planting (D4) yielded the lowest (1,589.5 kg ha⁻¹). Paddy straw mulch produced high yield (fresh yield of 16941.36 kg ha^-1) which was on par with and green leaves mulch (fresh yield of 15798.1 kg ha^-1). PCA is an adaptive data analysis technique for reducing the dimensionality of large data sets like weather parameters hence increasing the interpretability with minimum information loss. Regression equations were fitted estimating yield for green leaves; paddy straw and dry coconut leaves mulch by performing Principal Component Analysis (PCA). From analysis both the observed and estimated yields were comparable. This study investigates the effects of planting dates and mulching types on ginger yield in the face of fluctuating weather patterns, as well as the usefulness of statistical modelling like PCA and regression in yield prediction. This yield prediction models can be utilised by farmers and policy makers to predict yield in advance.