# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 4 Discussion
A limitation of our current calibration strategy that contributes to modest estimation performance at the field scale is that sub-grid scale heterogeneity in environmental conditions, including soil moisture, nutrient availability and resource competition from weeds, is not accounted for by the regional model, and probably most importantly, calibration does not differentiate between irrigated and rainfed crops because spatially explicit information about this practice is currently not available for MT. More specific model calibrations that account for irrigated and non-irrigated crops are expected to increase the precision of production estimates at sub-county scales. Moreover, the growing season in this study is assumed to extend from April to September for all seven MT crop types examined, which may not be accurate for each crop type. For example, winter wheat is planted in September or October and harvested the following July or August [45]. All of these factors likely contribute to the lower YieldM and YieldF correspondence. For the seven major crop types examined over the 2008 to 2015 study period, although the total planted area in MT is increasing (2.79 \(\times\) 10\({}^{5}\) hectares year\({}^{-1}\)), the planted area of each crop type shows large annual variability (Figure 8), which contributes to variations in annual crop production in addition to other influential factors including climate variability. Spring wheat, winter wheat and alfalfa are the three largest components of the total cropland area in MT (34.4%, 26.8% and 19.7%, respectively) from 2008 to 2015. The land dedicated to alfalfa increased by approximately 1.52 \(\times\) 10\({}^{5}\) hectares year\({}^{-1}\), and was also the largest contributor to annual variability in MT total planted area according to the NASS CDL record. The planted areas and estimated annual crop production for spring wheat and winter wheat did not show consistent trends during the study period (Figure 8). Simultaneously, barley, maize and peas showed increasing trends in planted area, but at relatively low levels (2.86, 0.48 and 3.73 \(\times\) 10\({}^{4}\) hectares year\({}^{-1}\) respectively), although the combined extent of all three crops occupied only 13.5% of the total planted cropland area and made only a small contribution to changes in total planted area and crop production for the state. Because Montana's agriculture is mostly rainfed and has relatively low diversification, planted areas and crop production are very sensitive to climate variations and to changes in agricultural pricing. The MT crop production for all selected crops showed peaks in 2010 and 2013, which had the largest growing season precipitation volumes during the eight-year study period (351.6 mm in 2010 and 336.4 mm in 2013 and approximately 24.6% and 19.2% above mean precipitation from 2008 to 2015), as well as large ratios of precipitation to potential evapotranspiration (0.45 and 0.40 for the years 2010 and 2013, respectively), indicating wet climate anomalies for these two years. Further research is needed to understand the sensitivity of annual crop yield and planted land variations to external factors such as climate fluctuations or changes in crop prices or cost of agricultural inputs. In recent decades, agriculture has continuously outpaced all other industries as a proportion of total gross domestic product (GDP) in MT, while agricultural production accounted for over 30% of the state's basic industry employment, labor income and gross sales [44], suggesting the importance for obtaining consistent crop yield assessments across the entire state. This study provides a potential approach for continuous crop yield monitoring at 30-m resolution, which may better inform agricultural management and policy decisions. However, further studies are needed to improve crop yield estimation accuracy and performance and clarify the role of socioeconomic factors, management practices and climate variability on crop yield patterns and trends.