Methods for environment: productivity trade-off analysis in agricultural systems

Trade-off analysis has become an increasingly important approach for evaluating system level outcomes of agricultural production and for prioritising and targeting management interventions in multi-functional agricultural landscapes. We review the strengths and weakness of different techniques available for performing trade-off analysis. These techniques, including mathematical programming and participatory approaches, have developed substantially in recent years aided by mathematical advancement, increased computing power, and emerging insights into systems behaviour. The strengths and weaknesses of the different approaches are identified and discussed, and we make suggestions for a tiered approach for situations with different data availability. This chapter is a modified and extended version of Klapwijk et al. (2014)

Emotional Contagion in a Texas Band Music Educators Facebook Group

The purpose of this study was to explore emotional sentiments expressed in user posts, comments, and media shared within the Texas Band Directors (TBD) Facebook group. Secondary data was web scraped using the Bardeen.AI Google Chrome extension. Linguistic inquiry word count (LIWC) software was used to identify percentage of positive and negative cognitive processes actively expressed by users within the TBD Facebook page. Of the 3,535 unique posts extracted from the TBD Facebook group, I identified 114 positive emotion and 53 negative emotion posts which accounted for 4.71% of all extracted posts. I coded these posts into the three categories defined in previous social media content analysis studies: curricular, co-curricular, and community. I identified 49 curricular, 73 co-curricular, and 40 community posts with high emotional sentiment scores. From there, I examined all comment threads from these original posts for evidence of emotional contagion. I found that 92.59% of the initially positive posts resulted in predominantly positive responses, while only 20.68% of the originally negative posts resulted in negative posts. These findings may suggest a need to better educate future music educators on how to navigate their social media use. While social media spaces can be beneficial places to gather and engage in professional discourse, more discussion may be needed regarding emotional regulation and navigation of in-person vs online self. Additionally, posts relating to curricular, co-curricular, and community topics show evidence of expression of emotional sentiments and emotional contagion spread within the TBD Facebook group

Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery

Rice ratooning has gained increasing interest in Asia as a way to boost rice production by allowing two rice harvests from a single growing season. Accurate mapping of this practice can improve rice production estimates. However, current efforts have mainly relied on optical sensors, which are limited by cloud cover, especially during the wet season when ratooning is common. This study systematically assessed the use of optical Sentinel-2, Synthetic Aperture Radar (SAR) Sentinel-1 data and their combination to map ratoon rice crops. Field data were collected in four provinces of the Philippines in 2018–19. Backscatter intensity from Sentinel-1, spectral information, and six commonly used vegetation indices (VIs) from Sentinel-2 were analysed using the Mann-Whitney U significance test to examine differences between the main and ratoon rice crops. Next, we compared the classification performance of decision tree (DT), support vector machine (SVM), and random forest (RF) classifiers. Results show that ratoon and main rice crop significantly differed in VV and VH polarisations, red edge and near-infrared bands, and all VIs. The highest accuracy was achieved with selected features in an RF classifier (overall accuracy of 92 %), compared to SVM (87 %) and DT (81 %). Classification using features from both Sentinel-1 and 2 consistently yielded higher accuracy than using features from one sensor alone. The total planting of ratoon rice was estimated at approximately 223 km2 (±4 % of the wet season rice area). This study demonstrates the value of combining SAR Sentinel-1 and optical Sentinel-2 for ratoon rice mapping

Sentinel-1 Backscatter Analysis of Ratoon Rice Crops: Example from Ratooning Practice in the Philippines

Ratooning is a common rice crop management practice where the plant is left to regrow from post-harvest stubble, providing a low-input second crop. There is rising interest and use of rice ratooning in Asia to increase productivity on the same amount of land hence an accurate ratoon rice detection is important for monitoring rice production and productivity. Synthetic Aperture Radar (SAR) time series have been widely used for rice crop monitoring but there is little research on detecting ratoon rice practice in rice cropping systems. Hence, this study aims to (1) investigate the temporal SAR backscatter signatures of ratoon rice crops compared to those of the main rice crop and (2) determine if the ratoon rice signature is consistent in irrigated and rainfed rice systems. Farmers’ interviews and field surveys were conducted in four provinces of the Philippines, where rice ratooning was reported in the dry, wet, and very wet growing seasons of 2018-19. Four bands of backscatter information (VV, VH, VH/VV, and the radar vegetation index (RVI)) were obtained from the multi-temporal Sentinel-1A and B data with a six-day repeat cycle. We determined which band and which period of the season showed significant differences between the main rice and ratoon rice crops. Our results show that ratoon rice significantly differed from the main rice crop during the peak of the growing season in the VH, VH/VV, and RVI bands. We also found that the signature of ratoon rice was the same (no significant difference) for irrigated and rainfed rice systems. These findings suggest that Sentinel-1 time series data is suitable for detecting ratoon rice in lowland irrigated and rainfed rice systems. Given the increased interest in rice ratooning, detecting ratoon rice and its expansion is important for monitoring rice management practices and rice production

Understanding the effects of bacterial leaf blight disease on rice spectral signature

Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv oryzae is considered as one of the most economically important diseases in tropical and temperate rice-growing areas. To manage the disease before reaching damaging levels, it is important to detect its occurrence and assess its intensity in large areas in timely and efficient manner. Traditional disease detection and assessment rely on manual observations in the field, which is often time-consuming, costly, and dependent on the ability of data collectors to accurately identify the disease. Remote sensing is an emerging technology for plant disease detection at different spatial scales. Remote sensing-based disease detection can provide valuable information for operational applications, such as designing low-cost agricultural cameras, mapping disease epidemics at different administrative levels, and estimating yield losses. Previous studies have revealed several spectral changes at the canopy level in BLB-infected rice plants but have focused only on a single development stage, such as the grain filling and maturity stages, which limits the ability to use remote sensing for early disease detection. To address this knowledge gap, a field experiment was conducted in the Philippines during the wet season of 2023, when conditions are favorable to BLB. IR24, a highly susceptible variety, was grown in the field. Hyperspectral signals were measured at the canopy level of inoculated and non-inoculated (control) plots from tillering to heading stages. We will present the spectral bands that could discriminate between BLB-infected and healthy plants and evaluate the separability of these spectral bands to identify the most sensitive bands at each development stage. The results from this study can support early detection and monitoring of BLB andprevent yield loss

MODIS NDVI Modified Z-score for Evaluating Drought Incidence of Rice Areas in the Mekong Delta

Extreme climate events such as flood, drought, and high temperature are expected to increase in frequency and intensity with climate change. Mapping and characterization of food production areas at risk can help in better targeting innovations and in enhancing the resilience of affected communities. In this study, we used two decades of the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI)[1][2] from 2003 to 2022 to map drought incidence in rice areas in the Mekong Delta, a densely populated region and an important source of rice for domestic and export markets

Temporal backscatter characterisation of ratoon rice crops based on Sentinel-1 intensity data

Ratooning, the cropping practice of harvesting a second crop from the stubbles of the primary harvest, is gaining renewed popularity as a resource-efficient alternative to increase rice production. Although current remote sensing-based rice monitoring systems have considered rice ratooning systems, nothing is known about the temporal backscatter response of ratoon rice, which is necessary for accurate rice ratooning detection in cloud-pervasive regions. Using backscatter time series from Sentinel-1A/B data, for the first time, we characterized the temporal backscatter signatures of ratoon rice crops in four features (VV and VH polarizations, the ratio of VH/VV, and the radar vegetation index (RVI)) to determine the optimal period and SAR features for main and ratoon rice discrimination. We also investigated the influence of harvesting methods on the backscatter of stubbles and the difference in backscatter between ratoon crops in irrigated and rainfed rice. We obtained data covering three growing seasons (2018–19), rice field boundaries and farmer interview data on cropping practices in the Philippines. The backscatter differences were assessed using the Mann–Whitney U and the Kruskal – Wallis test, while the classification was performed using partial least squares discriminant analysis (PLS-DA). We found that the observation during the peak of the growing season could best distinguish main and ratoon rice, specifically in the reproductive (VH, p = .010) and ripening phase (VH/VV, p = .089 and RVI, p = .089). The PLS-DA model at the reproductive phase performed better, with an overall accuracy of 68% (AUC = 0.70) than the model from the ripening phase (OA = 60%, AUC = 0.64). The backscatter of stubbles from mechanically harvested fields is not significantly different from that of manually harvested fields. We also found no significant backscatter difference in ratoon crops across different water managements throughout all growth phases. This study demonstrates the potential of SAR Sentinel-1 time series data to determine periods and SAR features for optimal main and ratoon rice discrimination, which offers advantages for future remote sensing-based rice ratooning mapping and rice production estimation

Spectral Signature Generalization and Expansion Can Improve the Accuracy of Satellite Image Classification

Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) signature generalization: spectral signatures are derived from multiple images within one season, but perhaps from different years; (2) signature expansion: spectral signatures are created with data from images acquired during different seasons of the same year; and (3) combinations of expansion and generalization. Using data for northern Laos, we assessed the quality of these different signatures to (a) classify the images used to derive the signature, and (b) for use in temporal signature extension, i.e., applying a signature obtained from data of one or several years to images from other years. When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly. In contrast, signature generalization did not improve classification. When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy. Signature expansion also performed poorly but multi-year signature generalization performed much better and this appears to be a promising approach in the temporal extension of spectral signatures for satellite image classification

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

Improved rice crop and water management practices that make the sustainable use of resources more efficient are important interventions towards a more food secure future. A remote sensing-based detection of different rice crop management practices, such as crop establishment method (transplanting or direct seeding), can provide timely and cost-effective information on which practices are used as well as their spread and change over time as different management practices are adopted. Establishment method cannot be easily observed since it is a rapid event, but it can be inferred from resulting observable differences in land surface characteristics (i.e. field condition) and crop development (i.e. delayed or prolonged stages) that take place over a longer time. To examine this, we used temporal information from Synthetic Aperture Radar (SAR) backscatter to detect differences in field condition and rice growth, then related those to crop establishment practices in Nueva Ecija (Philippines). Specifically, multi-temporal, dual-polarised, C-band backscatter data at 20m spatial resolution was acquired from Sentinel-1A every 12 days over the study area during the dry season, from November 2016 to May 2017. Farmer surveys and field observations were conducted in four selected municipalities across the study area in 2017, providing information on field boundaries and crop management practices for 61 fields. Mean backscatter values were generated per rice field per SAR acquisition date. We matched the SAR acquisition dates withthe reported dates for land management activities and with the estimated dates for when the crop growth stages occurred. The Mann-Whitney U test was used to identify significant differences in backscatter between the two practices during the land management activities and crop growth stages. Significant differences in cross-polarised, co-polarised and band ratio backscatter values were observed in the early growing season, specifically during land preparation, crop establishment, rice tillering and stem elongation. These findings indicate thepossibility to discriminate crop establishment methods by SAR at those stages, suggesting that there is more opportunity for discrimination than has been presented in previous studies. Further testing in a wider range of environments, seasons, and management practices should be done to determine how reliably rice establishment methods can be detected. The increased use of dry and wet direct seeding has implications for many remote sensing-based rice detection methods that rely on a strong water signal (typical of transplanting) during the early season