Agriculture

Pubrica experts employ a rigorous six-step protocol based on the PRISMA statement to ensure high scientific quality and transparency while conducting systematic review and meta-analyses. This process includes scoping, planning, comprehensive literature search, screening, eligibility assessment, and detailed presentation of findings. The process has been chosen to guarantee the scientific quality and ensure a transparent, systematic review.

We assist in crafting robust research methodologies tailored to the specific needs of your agricultural study, ensuring that each design maximizes the accuracy and relevance of your research outcomes. Whether you need to design an experimental study (randomized complete block design, factorial, spilt-plot design, repeated measures design), observational designs (cohort, case-control, cross sectional), longitudinal, survey, simulation model, participatory evaluation, case studies, conceptual methods (e.g. review, framework development), or for methodologies for AI and ML, Pubrica’s expertise can draft it.

Our team provides extensive data science support, including sophisticated statistical analyses (General linear regression model, regression analyses, clustering, time-series) and data analytics using AI and ML (sentiment, association rules, log analysis, factor analysis, anomaly detection, neural network, predictive analytics such as adaptive neural network model, descriptive, diagnostic analytics, prescriptive analytics), tailored to agricultural research manuscripts. We focus on delivering actionable insights that drive informed decisions in agricultural practices and policies.