Harvested area gaps in China between 1981 and 2010:Effects of climatic and land management factors

Previous analyses have shown that cropland in China is intensifying, leading to an increase in crop production. However, these output measures leave the potential for further intensification largely unassessed. This study uses the harvested area gap (HAG), which expresses the amount of harvested area that can be gained if all existing cropland is harvested as frequently as possible, according to their potential limit for multi-cropping. Specifically, we calculate the HAG and changes in the HAG in China between 1981 and 2010. We further assess how climatic and land management factors affect these changes. We find that in China the HAG decreases between the 1980s and the 1990s, and subsequently increases between the 1990s and the 2000s, resulting in a small net increase for the entire study period. The initial decrease in the HAG is the result of an increase in the average multi-cropping index throughout the country, which is larger than the increase in the potential multi-cropping index as a result of the changed climatic factors. The subsequent increase in the HAG is the result of a decrease in average multi-cropping index throughout the country, in combination with a stagnant potential. Despite the overall increase in harvested area in China, many regions, e.g. Northeast and Lower Yangtze, are characterized by an increased HAG, indicating their potential for further increasing the multi-cropping index. The study demonstrates the application of the HAG as a method to identify areas where the harvested area can increase to increase crop production, which is currently underexplored in scientific literature

Impact of cropland displacement on the potential crop production in China:a multi-scale analysis

Changes in the amount and location of cropland areas may affect the potential crop production at different spatial scales. However, most studies ignore the impacts of cropland displacement on potential crop production. In many countries, cropland protection policies mainly aim for no loss in cropland area, while there is no restriction on change of cropland location. Taking China as the study area, we analyze the impacts of cropland displacement on potential crop production at four administrative levels during the period 2000 and 2018. At the national level, we find a net decrease in cropland area of 0.81 Mha, while another 19.63 Mha was displaced. The former led to a decrease of 4.20 Mton in potential crop production, while the latter resulted in a decrease of 43.26 Mton as a result of lower quality of the newly cultivated lands. In other words, cropland displacement explains 91% of the total loss in potential crop production at the national scale. However, the contribution of cropland displacement to total change in potential crop production is increasingly smaller at provincial level, municipal level, and county levels. These findings highlight the importance of geographic location on crop production and suggest that cropland policies should consider geographic location in addition to cropland area

Aboveground net primary productivity of vegetation along a climate-related gradient in a Eurasian temperate grassland: spatiotemporal patterns and their relationships with climate factors

Accurate assessments of spatiotemporal patterns in net primary productivity and their links to climate are important to obtain a deeper understanding of the function, stability and sustainability of grassland ecosystems. We combined a satellite-derived NDVI time-series dataset and field-based samples to investigate spatiotemporal patterns in aboveground net primary productivity (ANPP), and we examined the effect of growing season air temperate (GST) and precipitation (GSP) on these patterns along a climaterelated gradient in an eastern Eurasian grassland. Our results indicated that the ANPP fluctuated with no significant trend during 2001-2012. The spatial distribution of ANPP was heterogeneous and decreased from northeast to southwest. The interannual changes in ANPP were mainly controlled by year-to-year GSP; a strong correlation of interannual variability between ANPP and GSP was observed. Similarly, GSP strongly influenced spatial variations in ANPP, and the slopes of fitted linear functions of the GSP-ANPP relationship increased from arid temperate desert grassland to humid meadow grassland. An exponential function could be used to fit the GSP-ANPP relationship for the entire region. An improved moisture index that combines the effects of GST and GSP better explained the variations in ANPP compared with GSP alone. In comparisons with the previous studies, we found that the relationships between spatiotemporal variations in ANPP and climate factors were probably scale dependent. We imply that the quantity and spatial range of analyzed samples contribute to these different results. Multi-scale studies are necessary to improve our knowledge of the response of grassland ANPP to climate change.ArticleENVIRONMENTAL EARTH SCIENCES.76(1):56(2017)journal articl

An improved YOLOv5s model for assessing apple graspability in automated harvesting scene

IntroductionWith continuously increasing labor costs, an urgent need for automated apple- Qpicking equipment has emerged in the agricultural sector. Prior to apple harvesting, it is imperative that the equipment not only accurately locates the apples, but also discerns the graspability of the fruit. While numerous studies on apple detection have been conducted, the challenges related to determining apple graspability remain unresolved.MethodsThis study introduces a method for detecting multi-occluded apples based on an enhanced YOLOv5s model, with the aim of identifying the type of apple occlusion in complex orchard environments and determining apple graspability. Using bootstrap your own atent(BYOL) and knowledge transfer(KT) strategies, we effectively enhance the classification accuracy for multi-occluded apples while reducing data production costs. A selective kernel (SK) module is also incorporated, enabling the network model to more precisely identify various apple occlusion types. To evaluate the performance of our network model, we define three key metrics: APGA, APTUGA, and APUGA, representing the average detection accuracy for graspable, temporarily ungraspable, and ungraspable apples, respectively.ResultsExperimental results indicate that the improved YOLOv5s model performs exceptionally well, achieving detection accuracies of 94.78%, 93.86%, and 94.98% for APGA, APTUGA, and APUGA, respectively.DiscussionCompared to current lightweight network models such as YOLOX-s and YOLOv7s, our proposed method demonstrates significant advantages across multiple evaluation metrics. In future research, we intend to integrate fruit posture and occlusion detection to f]urther enhance the visual perception capabilities of apple-picking equipment

From meta-studies to modeling: Using synthesis knowledge to build broadly applicable process-based land change models

International audienceThis paper explores how meta-studies can support the development of process-based land change models (LCMs) that can be applied across locations and scales. We describe a multi-step framework for model development and provide descriptions and examples of how meta-studies can be used in each step. We conclude that meta-studies best support the conceptualization and experimentation phases of the model development cycle, but cannot typically provide full model parameterizations. Moreover, meta-studies are particularly useful for developing agent-based LCMs that can be applied across a wide range of contexts, locations, and/or scales, because meta-studies provide both quantitative and qualitativedata needed to derive agent behaviors more readily than from case study or aggregate data sources alone. Recent land change synthesis studies provide sufficient topical breadth and depth to support the development of broadly applicable process-based LCMs, as well as the potential to accelerate the production of generalized knowledge through model-driven synthesis

Environmental cognitions mediate the causal explanation of land change

Causal explanations of land change are fundamental in land system science, yet existing findings are difficult to synthesize due to the imprecise terminology and the various analytical frameworks they have applied. This paper compares three existing conceptual frameworks, in terms of underlying driving forces and proximate causes, actors, and environmental cognitions, by aligning the relevant elements into a causal chain. We find that the elicitation of environmental cognitions helps in providing a detailed description of this causal chain. By synthesizing case study evidence on agricultural land change into the generalized causal chain, we find that the effects of underlying driving forces on land change have been substantially mediated by environmental cognitions. Operationalizing environmental cognitions requires more efforts than regular actor-based studies, but a proper understanding of its mediating role should be accounted for in local scale studies and is essential for human-centred policy design

The complexity of measuring cropland use intensity: An empirical study

CONTEXT: Cropland intensification promises additional food supply without further expanding croplands into natural ecosystems. Cropland use intensity measures the degree of intensification by various indicators, both from input and output perspectives; all of these indicators are however not always coherent—which may cause confusion and send contradicting signals to policymakers. Few empirical studies have been conducted to relate and compare various intensity indicators. OBJECTIVE: In this study, we start with a hypothesis that cropland use intensity measured by different indicators may differ. We then test such a hypothesis based on empirical evidence in a typical multi-cropped region (i.e., Jinxian County) in Jiangxi Province—a major breadbasket in Southern China. METHODS: We focus on two widely used indicators, i.e., multi-cropping frequency (MCF) and crop growth duration (GDa), measured by a hybrid time-series remote sensing dataset which fuses MODIS and Gaofen-1 images for the year 2015. We map these two indicators independently at a 16 m spatial resolution. For each pixel, MCF takes value from 1 to 3 corresponding to single-, double- and triple- cropping; while GDa is quantified by the accumulative crop growth days within the study year. We relate the values of two indicators, summarize the descriptive statistics of GDa grouped by MCF categories, and compare MCF and GDa values by using a box-whisker chart, a bivariate map, and a set of statistical tests. RESULTS AND CONCLUSIONS: We find that a significant overlap of GDa exists between single cropping and double cropping both visually and statistically. In other words, the different cropland intensity levels measured by MCF appear to be the same if measured by GDa. The box-whisker chart shows that the GDa of single cropping in the upper quarter is substantially longer than the GDa of double cropping in the lower quarter. The non-parametric tests show that the overlapped GDa between single cropping and double cropping exists substantially, which may cause confusion. Moreover, the bivariate map shows that single cropping with shorter growth duration (i.e., consistent low intensity) and double cropping with longer growth duration (i.e., consistent high intensity) account for 44% and 28%, respectively, while the rest manifested as inconsistent which accounts for another 28% of the total cropland area. SIGNIFICANCE: These results confirm our hypothesis—the inconsistency among intensification indicators. Different measurements may convey contradicting messages for policymakers in pursuing the sustainable intensification goals, which suggests more efforts are required to understand the multidimensionality of the process of cropland intensification for distinct policy measurements

Crop Land Change Detection with MC&N-PSPNet

To enhance the accuracy of agricultural area classification and enable remote sensing monitoring of agricultural regions, this paper investigates classification models and their application in change detection within rural areas, proposing the MC&N-PSPNet (CBAM into MobileNetV2 and NAM into PSPNet) network model. Initially, the HRSCD (High Resolution Semantic Change Detection) dataset labels undergo binary redrawing. Subsequently, to efficiently extract image features, the original PSPNet (Pyramid Scene Parsing Network) backbone network, ResNet50 (Residual Network-50), is substituted with the MobileNetV2 (Inverted Residuals and Linear Bottlenecks) model. Furthermore, to enhance the model’s training efficiency and classification accuracy, the NAM (Normalization-Based Attention Module) attention mechanism is introduced into the improved PSPNet model to obtain the categories of land cover changes in remote sensing images before and after the designated periods. Finally, the final change detection results are obtained by performing a different operation on the classification results for different periods. Through experimental analysis, this paper demonstrates the proposed method’s superior capability in segmenting agricultural areas, which is crucial for effective agricultural area change detection. The model achieves commendable performance metrics, including overall accuracy, Kappa value, MIoU, and MPA values of 95.03%, 88.15%, 93.55%, and 88.90%, respectively, surpassing other models. Moreover, the model exhibits robust performance in final change detection, achieving an overall accuracy and Kappa value of 93.24% and 92.29%, respectively. The results of this study show that the MC&N-PSPNet model has significant advantages in the detection of changes in agricultural zones, which provides a scientific basis and technical support for agricultural resource management and policy formulation

A cultivated planet in 2010 - Part 2: The global gridded agricultural-production maps

Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns; thus they are less informative for subsequent spatially explicit agricultural and environmental analyses. In the second part of the two-paper series, we introduce SPAM2010 – the latest global spatially explicit datasets on agricultural production circa 2010 – and elaborate on the improvement of the SPAM (Spatial Production Allocation Model) dataset family since 2000. SPAM2010 adds further methodological and data enhancements to the available crop downscaling modeling, which mainly include the update of base year, the extension of crop list, and the expansion of subnational administrative-unit coverage. Specifically, it not only applies the latest global synergy cropland layer (see Lu et al., submitted to the current journal) and other relevant data but also expands the estimates of crop area, yield, and production from 20 to 42 major crops under four farming systems across a global 5 arcmin grid. All the SPAM maps are freely available at the MapSPAM website (http://mapspam.info/, last access: 11 December 2020), which not only acts as a tool for validating and improving the performance of the SPAM maps by collecting feedback from users but is also a platform providing archived global agricultural-production maps for better targeting the Sustainable Development Goals. In particular, SPAM2010 can be downloaded via an open-data repository (DOI: https://doi.org/10.7910/DVN/PRFF8V; IFPRI, 2019).PRIFPRI3; DCA; ISI; CRP2; 1 Fostering Climate-Resilient and Sustainable Food SupplyEPTD; PIMCGIAR Platform for Big Data in Agriculture (Big Data); CGIAR Research Program on Policies, Institutions, and Markets (PIM

Harvested area gaps in China between 1981 and 2010: Effects of climatic and land management factors

Previous analyses have shown that cropland in China is intensifying leading to an increase in crop production. However, these output measures leave the potential for further intensification largely unassessed. This study uses the harvested area gap (HAG), which expresses the amount of harvested area that can be gained if all existing cropland is harvested as frequently as possible, according to their potential limit for multi-cropping. Specifically, we calculate the HAG and changes in the HAG in China between 1981 and 2010. We further assess how climatic and land management factors affect these changes. We find that in China the HAG decreases between the 1980s and the 1990s, and subsequently increases between the 1990s and the 2000s, resulting in a small net increase for the entire study period. The initial decrease in the HAG is the result of an increase in the average multi-cropping index throughout the country, which is larger than the increase in the potential multi-cropping index as a result of the changed climatic factors. The subsequent increase in the HAG is the result of a decrease in average multi-cropping index throughout the country, in combination with a stagnant potential. Despite the overall increase in harvested area in China, many regions, e.g. Northeast and Lower Yangtze, are characterized by an increased HAG indicating their potential for further increasing the multi-cropping index. The study demonstrates the application of the HAG as a method to identify areas where the harvested area can increase to increase crop production, which is currently underexplored in scientific literature.PRIFPRI3; DCA; ISI; CRP2; Capacity Strengthening; 1 Fostering Climate-Resilient and Sustainable Food SupplyEPTD; PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM