# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
## 1 Introduction  
Midwestern US agriculture is dominated by corn (_Zea mays_ L.) and soybean (_Glycine max_ [L.] Merr.) production and both crops are important export commodities. In 2017, corn and soybean production in the United States made up 33% and 34% of total world production, and 56% and 46% of US production came from the Corn Belt states (Iowa, Nebraska, Indiana, Illinois, and Kansas), respectively [1, 2]. Vast agricultural areas are used in the production of these crops which can impact gross primary production (GPP) and the global carbon cycle. The Midwestern US region was found to have one of the highest GPP rates in the northern hemisphere, owing to the large-scale production of corn, a C4 crop [3]. In addition, conventional corn and soybean production with bare soil in the off-season and frequent tillage events can impact GPP compared to reduced till systems [4]. Despite recent improvements incrop and soil management, no-till and cover crop systems only make up 35% and 4% in Iowa in 2017, respectively, meaning that conventional systems are prevailing [5]. Due to the economic and ecological importance of this region, the quantification of regional GPP as a proxy for yield as well as an essential part of the carbon cycle, remains imperative. The improvement of GPP estimations on a regional scale may also help to assess how changes in crop and soil management impact GPP and yield over time and may be used as decision support tool for climate-smart agriculture and best-practice management in terms of carbon cycling, soil quality, and crop performance.  
There have been different approaches to quantify GPP from satellite images, such as the MODerate resolution Imaging Spectroradiometer (MODIS) satellite GPP product (MOD17A2) (referred to as GPPMODIS); however, the accuracy of these estimates has been limited to a regional scale [6; 7]. Therefore, there have been different approaches proposed to improve the GPPMODIS estimate. MODIS products have been included in a biophysical model to calculate GPP on a global level, which showed good agreement to eddy covariance (EC) derived GPP [8; 9]. Another modelling approach using MODIS imagery products could also improve GPP measurements in a Douglas fir stand [10]. Other models have been proposed in literature to ameliorate GPPMODIS [11]. A more parsimonious approach is the improvement of the maximum radiation use efficiency (RUE\({}_{\text{max}}\)) and the fraction of absorbed photosynthetic active radiation (fPAR) estimates with field measurements and EC-derived GPP, as well as the use of land use maps [12; 13; 14; 15]. In an analysis of GPPMODIS for Africa, it was stated that the MODIS biome parameters need further improvement [16]. This is also true for other biomes worldwide [17]. Accordingly, new input parameters were used to up-scale GPP from field to whole regions in the Midwestern US using MODIS products [12; 13]. However, previous studies in the Midwestern US were either constrained by the density of ground-based EC stations (i.e., number of EC stations per area), or by the lack of field-based measurements of RUE\({}_{\text{max}}\) or fPAR [12; 13]. In addition, new land use maps were developed to distinguish crop fields with higher accuracy [18]. The aim of this study was to up-scale GPP with MODIS satellite image products from field to county using eight EC stations distributed across four counties in Iowa, additional field-based canopy reflectance measurements, and improved land use maps for the growing season of 2016. Therefore, crop-specific RUE\({}_{\text{max}}\) were calculated, and GPP products evaluated.