Yield Monitor Research Articles

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R2 was 0.9109, and RMSE was 0.91277 t.ha−1. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.

End-to-end deep learning for directly estimating grape yield from ground-based imagery

Yield estimation prior to harvest is a powerful tool in vineyard management, as it allows growers to fine-tune management practices to optimize yield and quality. However, yield estimation is currently performed using manual sampling, which is time-consuming and imprecise. This study demonstrates the applicability of nondestructive proximal imaging combined with deep learning for yield estimation in vineyards. Continuous image data collection using a vehicle-mounted sensing kit combined with collection of ground truth yield data at harvest using a commercial yield monitor allowed for the generation of a large dataset of 23,581 yield points and 107,933 images. Moreover, this study was conducted in a commercial vineyard which was mechanically managed, representing a challenging environment for image analysis but a common set of conditions in the California Central Valley. Three model architectures were tested: object detection, CNN regression, and transformer models. The object detection model was trained on hand-labeled images to localize grape bunches, and detections were either counted or their pixel count was summed to obtain a metric which was correlated to grape yield. Conversely, regression models were trained end-to-end to directly predict grape yield from image data without the need for hand labeling. Results demonstrated that both a transformer model as well as the object detection model with pixel area processing performed comparably, with a mean absolute percent error of 18% and 18.5%, respectively on a representative holdout dataset. Saliency mapping was used to demonstrate the attention of the CNN regression model was localized near the predicted location of grape bunches, as well as on the top of the grapevine canopy. Overall, the study demonstrated the applicability of proximal imaging and deep learning for prediction of grapevine yield on a large scale. Additionally, the end-to-end modeling approach was able to perform comparably to the object detection approach while eliminating the need for hand-labeling.

SOYBEAN YIELD FORECAST USING DUAL-POLARIMETRIC C-BAND SYNTHETIC APERTURE RADAR

Abstract. Crop yield forecast is important, but determining productivity is an on-going challenge for agricultural communities including policy makers. An increase in the number of satellites, improved temporal and spatial resolutions and more open data policies, are leading to wider interest by the agricultural sector in exploiting space-based data at moderate spatial resolutions (10–30 m) and varying wavelengths (optical and microwave) for crop yield monitoring. This study evaluated Sentinel-1 dual-polarimetric data to forecast soybean yields one month before the harvest at field scales over a site in central Argentina. Specifically, polarimetric features were extracted from the Sentinel-1 data using the M-Chi decomposition. An Artificial Neural Network (ANN) model was trained using a time series of the single-bounce and volume scattering parameters derived from M-Chi from stacks of data acquired during the growing season. To estimate soybean yield from the ANN model, an innovative iterative retrieval method was developed. This retrieval approach improved the accuracies of the soybean yield forecast delivering a final coefficient of determination (R2) of 0.81 with a root mean square error (RMSE) of 755.81 kg/ha and mean absolute error (MAE) of 581.65 kg/ha. These accuracies demonstrate a high potential of Synthetic Aperture Radar (SAR) data from Sentinel-1 for soybean yield forecast at field scales.

Organic–inorganic hybrid perovskite scintillators for mixed field radiation detection

AbstractSensitive and fast detection of neutrons and gamma rays is vital for homeland security, high‐energy physics, and proton therapy. Fast‐neutron detectors rely on light organic scintillators, and γ‐ray detectors use heavy inorganic scintillators and semiconductors. Efficient mixed‐field detection using a single material is highly challenging due to their contradictory requirements. Here we report hybrid perovskites (C8H12N)2Pb(Br1−xClx)4 that combine light organic cations and heavy inorganic skeletons at a molecular level to achieve unprecedented performance for mixed‐field radiation detection. High neutron absorption due to a high density of hydrogen, strong radiative recombination within the highly confined [PbX6]4− layer, and sub‐nanometer distance between absorption sites and radiative centers, enable a light yield of 41 000 photons/MeV, detection pulse width of 2.97 ns and extraordinary linearity response toward both fast neutrons and γ‐rays, outperforming commonly used fast‐neutron scintillators. Neutron energy spectrum, time‐of‐flight based fast‐neutron/γ‐ray discrimination and neutron yield monitoring were all successfully achieved using (C8H12N)2Pb(Br0.95Cl0.05)4 detectors. We further demonstrate the monitoring of reaction kinetics and total power of a nuclear fusion reaction. We envision that molecular hybridized scintillators open a new avenue for mixed‐field radiation detection and imaging.image

Obtaining and Validating High-Density Coffee Yield Data

Coffee producers are ever more interested in understanding the dynamics of coffee’s spatial and temporal variability. However, it is necessary to obtain high-density yield data for decision-making. The objective of this study is to evaluate the quality of yield data obtained through a yield monitor onboard a coffee harvester, as well as to evaluate the potential of the data collected over three harvests. The yield monitor validation data showed a high correlation (above R2 0.968) when compared with the data obtained by a wagon instrumented with load cells. It was also possible to obtain yield maps for three consecutive seasons, allowing the identification of their internal variability, as well as classifying regions that show alternating yield patterns between years as the expression of the biennial yield behavior manifested inside and along the field, in addition to the spatial variability. This result indicates that, in addition to knowing the spatial yield variability, the biennial variance information must also be considered in the strategies for site-specific management. Regions that presented high yield variance should be alternated according to the productive year (high and low yield) and not only in consideration of their yield variability as on the regions with more stable yield behavior over time. The use of yield data can help the producer make more assertive decisions for crop and farm management.

Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data

Agriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soya yield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA’s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%.

Filtering, editing, and cropping yield maps in a R environment with the package cleanRfield

AbstractCleaning yield monitor observations to remove erroneous points can improve the accuracy of yield estimates used for farm record keeping or on‐farm research data collection, but current practices are time‐intensive and cumbersome. cleanRfield is an open‐source R package to improve the efficiency of processing spatial agricultural data such as yield maps. Compared with current standard yield monitor data cleaning solutions, cleanRfield can read and interpret a broader range of input data formats. Other key features of cleanRfield include automatic field boundary delineation and batch processing of data from multiple fields. In this Scientific Note, we overview functions within the cleanRfield package and introduce an integrative pipeline to evaluate and visualize yield monitor data. The package is being distributed under the GNU General Public License 2, and a more detailed tutorial including downloading instructions is available at https://github.com/filipematias23/cleanRfield.

Study of the dynamics of grass yield within the agricultural landscape based on long-term monitoring

Yield monitoring of a five-component grass stand in a breeding field within a moraine hill was carried out in 2003- 2018 in order to find patterns of its dynamics in the agrolandscape. The grass was used in the conditions of the Tver region without fertilizers in a single-cut mode in a field crossing all microlandscape positions of the hill (transcct) and divided into 30 plots. Annual data on productivity of grass mixture on each plot are presented in the form of: 1) absolute yield (specific hay yield); 2) relative yield (expressed as a percentage to maximum yield on a transect in a definite year). It was revealed that the yield of grasses is maximum in the central parts (≈ 6.5 t/ha) and minimal in the upper parts of the hillsides and at the top (≈ 5.2 t/ha). Time variability of yield depends on the geological structure of the soils ‒ increases with the transition from powerful binomials (≈ 40 %) to low-powerful ones (≈ 65 %). Correlation analysis showed that the transition to relative units (% of the yield) can significantly reduce the impact of climate on data variability. The maximum coincidence of temporal dynamics of different yield indicators is noted at the upper elevations ‒ here climatic conditions do not have a significant impact on the yield, as on slopes. Changes in the sum of the active temperatures have the greatest influence on the yield. Average indicators of correlation coefficients of yield and temperature sums are negative on the plots of soil distribution on powerful and medium-powerful binomials and positive on soils of low-powerful binomials. There has been found an inversely proportional dependence of the absolute yield of grasses on the sums of active temperatures in the pre-cut periods and in September and directly proportional in the second half of summer. Increasing the sum of temperatures contributes to a decrease in relative yields on sandy soils and optimizes the production process on loams and light loams. Dispersion analysis has shown that the main influence of temperatures on absolute yield is exerted by soil hydromorphism. The degree of impact of temperatures on relative yield depends on the nature of water exchange in the agrolandscape, the exposure of slopes (energy) and granulometric composition of soils. The results will make it possible to adapt measures for the placement of grass stands, regulation of water and thermal regimes of soils to farm conditions.

Fruit yield prediction and estimation in orchards: A state-of-the-art comprehensive review for both direct and indirect methods

• A systematic review of orchard yield prediction and estimation studies. • Summary of input features and modeling algorithms for indirect yield prediction. • Summary of applied platforms and counting strategies for direct yield estimation. • Application of engineering technologies needs coordinating with agronomic. • Recommendations of promising technologies to be use for different planting areas. Orchard pre-harvest yield data is important for fruit growers, which can be used for economic benefit evaluation, management mode adjustment and so on. However, traditional manual operation by sampling estimation is quite an onerous and time-consuming task. The main approach of automatic yield monitoring is by establishing multi-information comprehensive prediction systems or using intelligent equipment. A review is performed to investigate and analyze the past 12 years (from 2010 to 2021) of research work regarding orchard yield prediction and estimation. According to our investigation, the most widely used input features in yield prediction systems are various vegetation indices information of plants, while machine learning is the most modeling method applied. In addition, machine vision systems based on image processing and deep learning have been developed rapidly in the field of agriculture and is also widely applied in orchard yield prediction. Finally, major challenges and countermeasures for orchard yield prediction and estimation are discussed. Complicated natural environments with inconsistent horticultural management practices are still critical challenges for large scale commercialization of researched yield prediction methods. Therefore, combination of engineering technology optimization and standardization of agronomic management practices are necessary to realize automatic operation in complex agricultural fields. The review is intended to summarize the development of yield prediction and estimation technologies and provided suggestions for orchard intelligent management.

Development and application experiments of a grain yield monitoring system

An impact-type grain yield monitoring system was developed and tested in laboratory and field to meet the requirement of precision agriculture in China. The system consisted of a mass flow sensor, a clean elevator speed sensor, a ground speed sensor, a moisture content sensor, a controller area network-bus processor, a global navigation satellite system receiver, and an industrial personal computer. The system was used to collect yield data and create the grid map in a field of winter wheat from 2018 to 2019. Laboratory and field calibrations were implemented to verify the accuracy of the mass flow sensor and the moisture content sensor. The errors of time delay and parking were removed, and exponential smoothing was performed in the data preprocessing. A yield reconstruction method was proposed to calculate the yield data of the sampling points based on the empirical model between flow voltage and grain weight. The predicted errors of total yield in both years were less than 5%, which indicated that the accuracy of yield monitoring system was high. An averaging method was used to generate the yield grid map. The spatiotemporal variation of the two-year yield was analyzed. Hierarchical management for precision farming was studied according to spatial variability (CVS = 31%) in 2018. Results showed that the temporal variability was CVT = 34%, which confirmed that the yield increased by about 0.3 t compared with the previous year. Moreover, the variability of the grid map showed the distribution of the yield reduction area, which was related to the characteristics of the seeds and the regional properties of the soil. The results proved that the developed yield monitoring system can be used as a tool to provide a reference for precision agriculture and high-yield management of winter wheat.

Livin' on the edge: Precision yield data shows evidence of ecosystem services from field boundaries

Field boundaries can provide ecosystem services to crops by creating better abiotic conditions for crop growth, and can also act as habitat for beneficial arthropods. This suggests that crop boundaries may create an intermediate hump-shaped increase in crop yield, where negative edge effects are cancelled out by increased ecosystem services from the field boundary. However, there is little large-scale evidence showing this, largely because plot-scale crop yields are costly and time-consuming to measure. Precision yield data from combine yield monitors has huge potential in this respect, as the equipment is widespread and data is frequently recorded by growers. In this study, we used 252 field-years of yield monitor data from three common crops – wheat (Triticum aestivum), canola (Brassica napus), and peas (Pisum sativum) – recorded across Alberta, Canada, and examined how yield varied with distances from common crop boundary types. Average crop yield tended to increase with distance from crop boundaries before plateauing at about 50 m, and yield variation (SD) tended to decrease with distance. There was evidence of an intermediate increase in yield for wheat away from shelterbelts, and a weak increase in canola, but this was not seen for other crop types or boundary types. This study represents one of the first uses of precision yield data to measure ecosystem service provision at large spatial scales.

The spatial variability of NDVI within a wheat field: Information content and implications for yield and grain protein monitoring.

Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was “averaged out” at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact wheat yield and GPC.

Tempering expectations on a novel biofuel tree: Seed and oil yield assessment of pongamia (Millettia pinnata) shows low productivity and high variability

Pongamia (Millettia pinnata) has recently gained attention as a novel biofuel feedstock. However, yield levels and dynamics of this largely undomesticated species remain poorly understood. This study addresses the critical lack of scientific evidence by collecting primary seed and oil yield data from 81 pongamia trees across a range of cultivation types in South India, and is the first to explore yield-determining factors using predictive regression models. Our results indicate that annual seed and oil yield for pongamia trees vary widely, generally staying below 2500 kg/ha and 1000 liter/ha, respectively. This current field performance is substantially lower than commonly reported figures in the literature. Furthermore, the study suggests that a complex interplay between genotype, environment and agronomy leads to large spatiotemporal variation in yields, and that this interplay remains poorly understood. Long-term yield monitoring is required to get better insights into realistic yield levels and mechanisms, before labeling pongamia as a significant, stable and profitable source of biofuel feedstock. The paper concludes with a set of hands-on guidelines for pongamia research and harvesting.

INFORMATION SOURCES IMPACT ON THE ADOPTION OF PRECISION TECHNOLOGY BY COTTON PRODUCERS IN THE UNITED STATES

Farmers demand more precision farming technology and specific information is consumed in the adoption process, which could not only depend on how efficiently the information flows among farmers. It is assumed that the sources of information are inputs in the production processes and could have an impact on the decision to adopt precision farming technology, after controlling for farmer and farm business characteristics. Accordingly, the objective of this research was to evaluate the effects of nine information sources used by cotton farmers of 12 U.S. producer states on the adoption of yield monitor with GPS, grid soil sampling, zone soil sampling, aerial photos, and soil survey maps. The scientific utility of this research lies on the quantitative data from the Southern Cotton Precision Farming Survey analysed using statistical methods such as univariate, bivariate and multivariate probit regressions. The statistical results indicated that information from dealers, consultants, university publications, and university events were the most common information sources used when searching for precision farming information with positive and significant effects on the adoption of precision farming technology. The presented findings from this comprehensive analysis can assist organizations in selecting information sources, and on planning communications and partnerships strategies fostering technology transfer.

Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models

In-season prediction of crop yield is a topic of research studied by several scientists using different methods. Seasonal forecasts provide critical insights to different stakeholders who use the information for strategic and tactical decisions. In this study, we propose a novel scalable method to forecast in season subfield crop yield through a machine learning model based on remotely sensed imagery and data from a process-based crop model on a cumulative crop drought index (CDI) designed to capture the impact of in-season crop water deficit on crops. To evaluate the performance of our proposed model, we used 352 growers' fields of different sizes across the states of Michigan, Indiana, Iowa, and Illinois, with 2520 respective yield maps generated by combine harvesters equipped with precise high-resolution yield monitor sensor, over multiple years (from 2006 up to 2019). We obtained high resolution digital elevation model, climate, and soil data to execute the SALUS model, a process-based crop model, to calculate the CDI for each field used in the study. We used Landsat Analysis Ready Dataset (ARD) products generated by USGS as image source to calculate the green chlorophyll vegetation index (GCVI). We found that the inclusion of the CDI in remote sensing-based random forest models substantially improved in-season subfield corn yield prediction. The addition of the CDI in the yield prediction model showed that the greatest improvements in predictions were observed in the driest year (2012) in our case study. The proposed approach also showed that the subfield spatial variations of corn yield are better captured with the inclusion of CDI for most fields. The earliest prediction in the growing season with GCVI and CDI together outperformed the latest prediction with GCVI alone, highlighting the potential of CDI for predicting spatial variability of maize yield around grain filling period, which is on average close to two months before typical crop harvest in the US Midwest.

Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models

One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions.

Design of IoT-based Mechatronic Module for Real-Time Yield Monitoring During Harvesting of Greenhouse Produce and Vineyards

Grape farmers are always underpaid by traders for their farm produce, due to the lack of precise assessment of the quality and quantity of the yield. After assessment of yield in grape vineyards, traders make part payment to the farmers and the balance amount is paid after the harvest is delivered to the market. The vineyard produce is damaged in terms of quality and quantity, by the time it reaches the market, due to reasons such as harvesting by inexperienced and unskilled laborers, labor shortage, physical damage during transit, etc. The work presented in this paper addresses the above-mentioned challenges through a custom-designed IoT-equipped Mechatronic module for real-time assessment of the yield of grapefruits harvested. The IoT-based weighing system is mounted on the robotic arm along with an integrated gripper cum cutter in order to monitor the weight of each harvested fruit and store the data on a server using the Internet. Field experiments were conducted during winter 2021 in the Pune district of Maharashtra and various greenhouse products were harvested like Bell Pepper, Long Chili Pepper, Bitter Melon, Guava, Eggplant, Okra in laboratory tests and Grapes in vineyards. The average weight of the variety of harvested fruits varies from 15 to 500 grams. This Precision Agriculture technology has reported an improved yield in terms of increased fruit shelf life. The developed system is tested and validated at ICAR-National Research Centre for Grapes, Pune. IoT implementation allows farmers to track various farm activities, and it helps to improve productivity in terms of yield, thus saving precious time and labor cost.

Examination of Biological and Economic Characteristics of Introduced Walnut Cultivars and Recommendations for Spreading

Plantation cultivation of grafted walnuts in Bosnia and Herzegovina did not exist until 6 years ago. Walnut seedlings (Juglans regia L) were mainly planted, which showed all the shortcomings related to the trees obtained from the seeds. In the last 5 years, there has been interest in planting grafted walnuts, so in this regard there was a need for controlled introduction of varieties suitable for this area.
 Research was conducted during 2020 - 2021 at four walnut tree cultivars, Chandler, Franquette, Fernor i Fernette.
 The aim of the research is to determine how certain varieties behave in agroecological conditions of the Majevica region and on that basis to give a proposal of the best variety for further spread in the area of this region. The research was conducted within the project number 02 / 9-712-1-5-3 / 21. The research was carried out at the Skakava Gornja site at an altitude of 192 m with a southwestern exposure (plot I) and a northeastern exposure at 172 m above sea level (plot II), with a terrain slope of 8%.
 The research related to vegetative growth included counting and measuring the average, then the minimum and maximum growth during two years. Yield monitoring included counting and measuring fruit number, average fruit size and calculating average yield.
 Generally the Chandler and Franquette varieties planted on the I plot had a stronger growth.
 Taking into account the vegetative growth, the number and size of fruits as well as the yield in the observed period, the Chandler and Franquette varieties are recommended for further expansion.

A method for soil management assessment in an unreplicated commercial field

Context Unreplicated trials are common in agriculture. However, statistical inferences differ from those of traditional experiments based on small, replicated plots. Aims To present a method to assess management effects on soil carbon (C) storage from unreplicated, side-by-side field trials. Methods Two estimates of means with spatially correlated errors are compared using a corrected t-statistic. Then causal inference is made by analysing a significant difference between the means (P < 0.05) and its changes over time. The use of the method is described in comparing yield and organic C stocks between two large fields. Yield was measured during 1997–2005 with a commercial yield monitor, and soil organic C stocks during 2003–2005. The fields experienced the same tillage practice until autumn 2003 and then with different tillage intensity. Key results The results show that crop C yield did not differ between the fields when using the same tillage practice but was greater in the tilled than the no-till field. The results also suggest that total and particulate organic matter-C contents depend on tillage history. For comparative purposes, the data were also analysed using standard mixed model analysis with a semivariogram model for spatial autocorrelation among the residuals. The mixed model results were generally similar to those of the corrected t-statistic method. The mixed model was often, but not always, less conservative than the corrected t-statistic model. Conclusions The method allows analysis of whole-field data and improves our understanding of soil C processes in commercial fields, where agricultural assessment cannot involve replication due to agronomic and economic constraints. Implications The method complements observational data analyses and can offer a direction towards whole-field management.

NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019

Abstract. An accurate paddy rice map is crucial for ensuring food security, particularly for Southeast and Northeast Asia. MODIS satellite data are useful for mapping paddy rice at continental scales but have a mixed-pixel problem caused by the coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time series Sentinel-1 and MODIS data. We demonstrated the method by generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (NESEA-Rice10). We compared the resultant paddy rice maps with available agricultural statistics at subnational levels and existing rice maps for some countries. The results demonstrated that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranged from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2=0.93) and Northeast China (R2=0.99). The spatial distribution of the 2017–2019 composite paddy rice map was consistent with that of the rice map from the International Rice Research Institute. The paddy rice planting area may have been underestimated in the region in which the flooding signal was not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.5281/zenodo.5645344 (Han et al., 2021a). Small examples can be found from the following DOI: https://doi.org/10.17632/cnc3tkbwcm.1 (Han et al., 2021b).