Background: Turmeric is a vital crop in Manipur, but its production is increasingly influenced by changing climatic conditions. Understanding the relationship between climate variables and turmeric output is essential for sustainable agricultural planning in the region. 
 
Methods: This study analyzes the causal relationship between climate factors and turmeric production in Manipur using secondary data from 2001-02 to 2020-21. Key climate variables examined include minimum temperature (&deg;C), relative humidity (RH%), wind speed (m/s), solar radiation (W/m&sup2;), and rainfall (mm). Statistical tools such as Compound Annual Growth Rate (CAGR), mean, standard deviation, standard error, and the Cuddy Della Valle Index (CDVI) were used to assess variability and trends. Additionally, the Correlogram and Augmented Dickey-Fuller (ADF) test was applied to data from 2013 to 2022 to evaluate the stationarity of climate and production variables. 
 
Result: The findings indicate high instability in turmeric cultivation area during Periods I and III, alongside moderate fluctuations in production and significant variability in rainfall and wind speed. The ADF test revealed stationary behavior in variables such as turmeric area, production, productivity, maximum RH, and wind speed. Conversely, temperature, wind direction, solar radiation, and rainfall exhibited non-stationary patterns. A strong positive correlation was found between turmeric production and factors like cultivation area, solar radiation, and maximum temperature. This study highlights the significant influence of climate variability on turmeric production in Manipur. The insights can guide policymakers and farmers in developing climate-resilient agricultural strategies to ensure stable turmeric yields in the future. 
&nbsp;

Study on Relationship between Climate Variability and Turmeric Production in Manipur; India

<div>Background: Price serves as signals of relative scarcity as well as abundance of a given agricultural product. Prices of agricultural products vary from month to month and even from day to day and this constitutes a source of risk to farmers whose livelihood depends on good pricing of their products. 
 
Method: This study was conducted to identify pattern of price dynamics and to develop model for price dynamics of beans, yellow maize and local rice using monthly price data of grains from the National Bureau of Statistics (2006-2015).&nbsp; 
 
Result: The rk of the beans followed a regular decline ranging from 0.4035 to 1 while those (rk) of yellow maize and local rice follow non regular decline with the range 0.1448 &ndash; 1(yellow maize) and 0.2945 -1 (local rice) thus indicating that the data are non-stationary. The corrected Akaike information criteria (AICC) and other information obtained for the ARIMA1,2,2 were higher than those of ARIMA2,2,2. The AICC obtained for the ARIMA1,2,2 are 958.422 (beans), 1178.169 (yellow Maize) and 1211.778 (local rice). The ARIMA2,2,2&nbsp;was thence&nbsp;favored above ARIMA1,2,2.</div>

Study on Relationship between Climate Variability and Turmeric Production in Manipur; India

Autoregressive Integration Moving Average (ARIMA) Model for Prices of Selected Grains in the South West Nigeria