Spatiotemporal Dynamics of Vegetation Net Primary Productivity and Its Response to Climate Change in Inner Mongolia from 2002 to 2019

Understanding vegetation dynamics and their responses to climate change are essential to enhance the carbon sequestration of the terrestrial ecosystem under global warming. Although some studies have identified that there is a close relationship between vegetation net primary productivity and climate change, it is unclear whether this response exists in ecologically fragile areas, especially in Inner Mongolia, in which multiple ecological ecotones are related to vegetation types. This study uses the Carnegie–Ames–Stanford Approach (CASA) model to estimate vegetation NPP in Inner Mongolia from 2002 to 2019 and focuses on the spatial and temporal changes of NPP of different vegetation types and their responses to three typical climate factors: precipitation, temperature, and solar radiation. The results show that the NPP estimated by the CASA model agrees well with the observed NPP (R2 = 0.66, p < 0.001). The vegetation NPP in Inner Mongolia decreases gradually from northeast to southwest, and the average NPP is 223.50 gC ∙ m−2. From 2002 to 2019, the NPP of all vegetation types trended upward, but exhibiting different rates. The vegetation types, ranked in order of decreasing NPP, are forest, cropland, grassland, and desert. The NPP response of different vegetation types to climate factors possesses significant differences. The cropland NPP and grassland NPP are mainly affected by precipitation, the desert NPP is controlled by both precipitation and solar radiation, and the forest NPP is determined by all three climate factors.

Dynamics of Vegetation Net Primary Productivity and Its Response to Drought in the Mongolian Plateau

Vegetation net primary productivity (NPP) is an important aspect of the global carbon cycle, and its change is closely related to climate change. This study analyzed the spatial-temporal variation of the standardized precipitation evapotranspiration index (SPEI) and NPP in the Mongolian Plateau, and investigated the effect of drought on NPP. To this end, NPP was simulated using the Carnegie-Ames-Stanford Approach (CASA) model. The results showed that from 1982 to 2014, NPP exhibited an upward trend in different seasons, and a significant increasing trend in most areas in the growing season and spring. The degree of drought also showed an increasing trend in each season. Moreover, the decrease in NPP and SPEI in Mongolia was larger than that in Inner Mongolia. Vegetation showed a positive correlation with SPEI in the growing season and summer, but a negative correlation in the other seasons. Moreover, the impact of drought on vegetation in the growing season showed a lag effect, whereas the lag response was inconspicuous during the early stages of the growing season. Different vegetation NPP responded strongly to the SPEI of the current month and the previous month.

Evaluation of NPP using three models compared with MODIS-NPP data over China

Estimating net primary productivity (NPP) is significant in global climate change research and carbon cycle. However, there are many uncertainties in different NPP modeling results and the process of NPP is challenging to model on the absence of data. In this study, we used meteorological data as input to simulate vegetation NPP through climate-based model, synthetic model and CASA model. Then, the results from three models were compared with MODIS NPP and observed data over China from 2000 to 2015. The statistics evaluation metrics (Relative Bias (RB), Pearson linear Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE)) between simulated NPP and MODIS NPP were calculated. The results implied that the CASA-model performed better than the other two models in terms of RB, RMSE, NSE and CC whether on the national or the regional scale. It has a higher CC with 0.51 and a smaller RMSE with 111.96 g C·m-2·yr-1 in the whole country. The synthetic model and CASA-model has the same advantages at some regions, and there are lower RMSE in Southern China (86.35 g C·m-2·yr-1), Xinjiang (85.53 g C·m-2·yr-1) and Qinghai-Tibet Plateau (93.22 g C·m-2·yr-1). The climate-based model has widespread overestimation and large systematic errors, along with worse performances (NSEmax = 0.45) and other metric indexes unsatisfactory, especially Qinghai-Tibet Plateau with relatively lower accuracy because of the unavailable observation data. Overall, the CASA-model is much more ideal for estimating NPP all over China in the absence of data. This study provides a comprehensive intercomparison of different NPP-simulated models and can provide powerful help for researchers to select the appropriate NPP evaluation model.

Sisters Walking Together: A Case Study of the CASA Model of Professional Midwifery Training

<p>Over the past three years there has been an increased focus on reducing global maternal mortality in developing countries. While substantial progress has been made, improvement remains slow in some areas. Making formal maternal healthcare services more acceptable, affordable and accessible to rural communities where the majority of maternal deaths occur, remains a considerable challenge. This study looks at the model of professional midwifery training employed at La Escuela de Partería Profesional de CASA [the CASA School of Professional Midwifery] in San Miguel de Allende, Mexico, whose aim is to train professional midwives who will provide culturally acceptable services in rural communities. It examines how the school's model reflects the development concept of community participation. This study adopted a single case study methodology to examine community participation at the CASA School of Professional Midwifery. Data collection techniques included the use of Semi-structured interviews, Focus Groups, Participant Observation and Document analysis. The study found that the CASA School included elements of participation within its model and highlights the different ways in which outsiders and insiders may arrive at implementing community participation-type processes in development initiatives. The study also finds that because of differences between the biomedical and development paradigms, the reality for many communities is that they are only permitted to participate in the maternal healthcare paradigms and models sanctioned by the state. The conclusion was that the CASA model of professional midwifery offers a new way to think about the relationship between maternal health professionals and the community, and of integrating communities back into the maternal health discourse.</p>

Sisters Walking Together: A Case Study of the CASA Model of Professional Midwifery Training

<p>Over the past three years there has been an increased focus on reducing global maternal mortality in developing countries. While substantial progress has been made, improvement remains slow in some areas. Making formal maternal healthcare services more acceptable, affordable and accessible to rural communities where the majority of maternal deaths occur, remains a considerable challenge. This study looks at the model of professional midwifery training employed at La Escuela de Partería Profesional de CASA [the CASA School of Professional Midwifery] in San Miguel de Allende, Mexico, whose aim is to train professional midwives who will provide culturally acceptable services in rural communities. It examines how the school's model reflects the development concept of community participation. This study adopted a single case study methodology to examine community participation at the CASA School of Professional Midwifery. Data collection techniques included the use of Semi-structured interviews, Focus Groups, Participant Observation and Document analysis. The study found that the CASA School included elements of participation within its model and highlights the different ways in which outsiders and insiders may arrive at implementing community participation-type processes in development initiatives. The study also finds that because of differences between the biomedical and development paradigms, the reality for many communities is that they are only permitted to participate in the maternal healthcare paradigms and models sanctioned by the state. The conclusion was that the CASA model of professional midwifery offers a new way to think about the relationship between maternal health professionals and the community, and of integrating communities back into the maternal health discourse.</p>

Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries

Apple (Malus domestica Borkh. cv. “Fuji”), an important cash crop, is widely consumed around the world. Accurately predicting preharvest apple fruit yields is critical for planting policy making and agricultural management. This study attempted to explore an effective approach for predicting apple fruit yields based on time-series remote sensing data. In this study, time-series vegetation indices (VIs) were derived from Planet images and analyzed to further construct an accumulated VI (∑VIs)-based random forest (RF∑VI) model and a Carnegie–Ames–Stanford approach (CASA) model for predicting apple fruit yields. The results showed that (1) ∑NDVI was the optimal predictor to construct an RF model for apple fruit yield, and the R2, RMSE, and RPD values of the RF∑NDVI model reached 0.71, 16.40 kg/tree, and 1.83, respectively. (2) The maximum light use efficiency was determined to be 0.499 g C/MJ, and the CASASR model (R2 = 0.57, RMSE = 19.61 kg/tree, and RPD = 1.53) performed better than the CASANDVI model and the CASAAverage model (R2, RMSE, and RPD = 0.56, 24.47 kg/tree, 1.22 and 0.57, 20.82 kg/tree, 1.44, respectively). (3) This study compared the yield prediction accuracies obtained by the models using the same dataset, and the RF∑NDVI model (RPD = 1.83) showed a better performance in predicting apple fruit yields than the CASASR model (RPD = 1.53). The results obtained from this study indicated the potential of the RF∑NDVI model based on time-series Planet images to accurately predict apple fruit yields. The models could provide spatial and quantitative information of apple fruit yield, which would be valuable for agronomists to predict regional apple production to inform and develop national planting policies, agricultural management, and export strategies.

Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

Response of net primary productivity to grassland phenological changes in Xinjiang, China

Determining the relationship between net primary productivity (NPP) and grassland phenology is important for an in-depth understanding of the impact of climate change on ecosystems. In this study, the NPP of grassland in Xinjiang, China, was simulated using the Carnegie-Ames-Stanford approach (CASA) model with Moderate Resolution Imaging Spectroradiometer (MODIS) grassland phenological (MCD12Q2) data to study trends in phenological metrics, grassland NPP, and the relations between these factors from 2001–2014. The results revealed advancement of the start of the growing season (SOS) for grassland in most regions (55.2%) in Xinjiang. The percentage of grassland area in which the end of the growing season (EOS) was delayed (50.9%) was generally the same as that in which the EOS was advanced (49.1%). The percentage of grassland area with an increase in the length of the growing season (LOS) for the grassland area (54.6%) was greater than that with a decrease in the LOS (45.4%). The percentage of grassland area with an increase in NPP (61.6%) was greater than that with a decrease in NPP (38.4%). Warmer regions featured an earlier SOS and a later EOS and thus a longer LOS. Regions with higher precipitation exhibited a later SOS and an earlier EOS and thus a shorter LOS. In most regions, the SOS was earlier, and spring NPP was higher. A linear statistical analysis showed that at various humidity (K) levels, grassland NPP in all regions initially increased but then decreased with increasing LOS. At higher levels of K, when NPP gradually increased, the LOS gradually decreased.

Spatiotemporal Variation in Ecosystem Services and Their Drivers among Different Landscape Heterogeneity Units and Terrain Gradients in the Southern Hill and Mountain Belt, China

Understanding the spatiotemporal heterogeneity of ecosystem services (ESs) and their drivers in mountainous areas is important for sustainable ecosystem management. However, the effective construction of landscape heterogeneous units (LHUs) to reflect the spatial characteristics of ESs remains to be studied. The southern hill and mountain belt (SHMB) is a typical mountainous region in China, with undulating terrain and obvious spatial heterogeneity of ESs, and was selected as the study area. In this study, we used the fuzzy k-means (FKM) algorithm to establish LHUs. Three major ESs (water yield, net primary productivity (NPP), and soil conservation) in 2000 and 2015 were quantified using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and Carnegie Ames-Stanford approach (CASA) model. Then, we explored the spatial variation in ESs along terrain gradients and LHUs. Correlation analysis was used to analyze the driving factors of ESs in each terrain region and LHU. The results showed that altitude and terrain niche increased along LHUs. Water yield and soil conservation increased from 696.86 mm and 3920.19 t/km2 to 1061.12 mm and 5117.90 t/km2, respectively, while NPP decreased from 666.95 gC/m2 to 648.86 gC/m2. The ESs in different LHUs differed greatly. ESs increased first and then decreased along LHUs in 2000. In 2015, water yield decreased along LHUs, while NPP and soil conservation showed a fluctuating trend. Water yield was mainly affected by precipitation, temperature and NDVI were the main drivers of NPP, and soil conservation was greatly affected by precipitation and slope. The driving factors of the same ES were different in different terrain areas and LHUs. The variation and driving factors of ESs in LHUs were similar to some terrain gradients. To some extent, LHUs can represent multiple terrain features. This study can provide important support for mountain ecosystem zoning management and decision-making.

Comprehensive Assessment Indicator of Ecosystem Resilience in Central Asia

The ecosystems in the arid inland areas of Central Asia are fragile and severely degraded. Understanding and assessing ecosystem resilience is a challenge facing ecosystems. Based on the net primary productivity (NPP) data estimated by the CASA model, this study conducted a quantitative analysis of the ecosystem’s resilience and comprehensively reflected its resilience from multiple dimensions. Furthermore, a comprehensive resilience index was constructed. The result showed that plain oasis’s ecosystem resilience is the highest, followed by deserts and mountainous areas. From the perspective of vegetation types, the highest resilience is artificial vegetation and the lowest is forest. In warm deserts, the resilience is higher in shrubs and meadows and lower in grassland vegetation. High coverage and biomass are not the same as the strong adaptability of the ecosystem. Moderate and slightly inelastic areas mainly dominate the ecosystem resilience of the study area. The new method is easy to use. The evaluation result is reliable. It can quantitatively analyze the resilience latitude and recovery rate, a beneficial improvement to the current ecosystem resilience evaluation.