Sardar Salar Saeed

Food security is a global concern, with around 690 million people suffering from food insecurity worldwide. The growing global population puts a strain on food demand and affecting the crop production. Food security is at the heart of the United Nation’s Sustainable Development Goals (SDGs) due to its global importance. Agricultural statistics are essential for achieving these goals. Agricultural census contains accurate information about crop production but it is costly. Earth observation can be useful resource to improve the crop production estimations. Long- term NDVI climatology of earth observation can capture the general agro-climatic conditions. SPOT- ProbaV NDVI series (1999-2020) was used to identify crop production system zones through ISO-DATA unsupervised classification. Then the relationship between these zones and administrative boundaries were assessed through stepwise multiple linear regression. Significant field-specific parameters, crop production system zones and administrative boundaries were merged together to quantify their combined impact on crop yield variability. Samples collection is very critical step for reliable and accurate crop production estimates. Earth observation explained only 23 percent yield variability whereas administrative boundaries based area frame sampling approach explained 39 percent.

Food security is a global concern, with around 690 million people suffering from food insecurity worldwide. The growing global population puts a strain on food demand and affecting the crop production. Food security is at the heart of the United Nation’s Sustainable Development Goals (SDGs) due to its global importance. Agricultural statistics are essential for achieving these goals. Agricultural census contains accurate information about crop production but it is costly. Earth observation can be useful resource to improve the crop production estimations. Long- term NDVI climatology of earth observation can capture the general agro-climatic conditions. SPOT- ProbaV NDVI series (1999-2020) was used to identify crop production system zones through ISO-DATA unsupervised classification. Then the relationship between these zones and administrative boundaries were assessed through stepwise multiple linear regression. Significant field-specific parameters, crop production system zones and administrative boundaries were merged together to quantify their combined impact on crop yield variability. Samples collection is very critical step for reliable and accurate crop production estimates. Earth observation explained only 23 percent yield variability whereas administrative boundaries based area frame sampling approach explained 39 percent.