Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.  However, using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.  Thus, we proposed a new approach to approximating irrigations of winter wheat over the North China Plain (NCP), where irrigation occurs extensively during the winter wheat growing season.  This approach used irrigation pattern parameters (IPPs) to define the irrigation frequency and timing.  Then, they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat (PRYM–Wheat), to improve the regional estimates of winter wheat over the NCP.  The IPPs were determined using statistical yield data of reference years (2010–2015) over the NCP.  Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield, with an increase and decrease in the correlation coefficient (R) and root mean square error (RMSE) of 0.15 (about 37%) and 0.90 t ha^–1 (about 41%), respectively.  The data in validation years (2001–2009 and 2016–2019) were used to validate PRYM–Wheat.  In addition, our findings also showed R (RMSE) of 0.80 (0.62 t ha^–1) on a site level, 0.61 (0.91 t ha^–1) for Hebei Province on a county level, 0.73 (0.97 t ha^–1) for Henan Province on a county level, and 0.55 (0.75 t ha^–1) for Shandong Province on a city level.  Overall, PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years, providing a scientific basis for ensuring regional food security.

Understanding the spatial distribution of the crop yield gap (YG) is essential for improving crop yields.  Recent studies have typically focused on the site scale, which may lead to considerable uncertainties when scaled to the regional scale.  To mitigate this issue, this study used a process-based and remote sensing driven crop yield model for winter wheat (PRYM-Wheat), which was derived from the boreal ecosystem productivity simulator (BEPS), to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.  Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield (Ya).  The distribution of Ya across the North China Plain showed great heterogeneity, decreasing from southeast to northwest.  The remote sensing-estimated results show that the average YG of the study area was 6 400.6 kg ha^–1.  The YG of Jiangsu Province was the largest, at 7 307.4 kg ha^–1, while the YG of Anhui Province was the smallest, at 5 842.1 kg ha^–1.  An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation, but there was a weak negative correlation between YG and accumulated temperature.  In addition, the YG was positively correlated with elevation.  In general, studying the specific features of the YG can provide directions for increasing crop yields in the future

Accurate rice area extraction and yield simulations are important for understanding how national agricultural policies and environmental issues affect regional spatial changes in rice farming. In this study, an improved regional parametric syntheses approach, that is, the rice zoning adaptability criteria and dynamic harvest index (RZAC-DHI), was established, which can effectively simulate the rice cultivation area and yield at the municipal level. The RZAC was used to extract the rice area using Moderate Resolution Imaging Spectroradiometer time-series data and phenological information. The DHI was calculated independently, and then yield was obtained based on the DHI and net primary productivity (NPP). Based on the above results, we analyzed the spatial–temporal patterns of the rice cultivation area and yield in Northeast China (NEC) during 2000–2015. The results revealed that the methods established in this study can effectively support the yearly mapping of the rice area and yield in NEC, the average precisions of which exceed 90 and 80%, respectively. The rice planting areas are mainly located on the Sanjiang, Songnen and Liaohe plains, China, which are distributed along the Songhua and Liaohe rivers. The rice cultivation area and yield in this region increased significantly from 2000 to 2015, with increases of nearly 58 and 90%, respectively. The rice crop area and yield increased the fastest in Heilongjiang Province, China, whereas small changes occurred in Jilin and Liaoning provinces, China. Their gravity centers exhibited evident northward and eastward shifts, with offset distances of 107 and 358 km, respectively. Moreover, Heilongjiang Province has gradually become the new main rice production region. The methodologies used in this study provide a valuable reference for other related studies, and the spatial-temporal variation characteristics of the rice activities have raised new attention as to how these shifts affect national food security and resource allocation.

Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing (RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies (DMCSs) over the Northeast China Plain (NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics (2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage (DVS)-based grain-filling algorithm and RS phenology information and leaf area index (LAI), had higher correlation (R, 0.61) and smaller root mean standard error (RMSE, 1.33 t ha^–1) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index (HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.

Estrogen plays an important role in regulating testicular Sertoli cell number. Furthermore, S-phase kinase-associated protein 2 (SKP2) plays a central role in mammalian cell cycle progression. The objective of this study was to determine whether 17β-estradiol can regulate the expression of SKP2, and the Sertoli cell cycle, via estrogen receptor β (ERβ), the cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA) and extracellular signal-regulated kinase (ERK1/2) pathway. When cultured immature boar Sertoli cells were treated with 17β-estradiol, a time-dependent increase in SKP2 mRNA and protein level was observed by real-time PCR and Western blot, and 17β-estradiol activity peaked at 30 min. Treatment with ICI182780 and ERβ antagonist reduced 17β-estradiol-induced expression of SKP2 and proliferating cell nuclear antigen (PCNA), while increasing the protein concentration of p27kip1. However, the effect of ERa antagonist on these parameters was lower than that of ICI182780 and ERβ. Forskolin had a similar effect as 17β-estradiol on the expression of SKP2, PCNA and p27kip1. Rp-cAMP, H-89 and U0126 treatment reduced 17β-estradiol-induced changes, while H-89 also inhibited ERK1/2 activation. Therefore, 17β-estradiol mainly regulates SKP2 mRNA and protein expression via ERβ-cAMP-PKA and ERK1/2 activation. SKP2 and PCNA expression were positively correlated, while increased SKP2 expression likely resulted in p27kip1 degradation.