Mathematical connections made during investigative tasks in statistics and probability

The Philippines has undergone a fundamental overhaul of its educational system to highlight basic education and overcome its deficiencies. The educational reform prompts prospective teachers to connect the concepts taught to instill a more profound understanding. As such, the researchers explored the mathematical connections made by prospective secondary mathematics teachers while completing investigative tasks. The study employed a concurrent triangulation mixed-methods design. Data were gathered from 39 prospective secondary mathematics teachers enrolled in the state universities' flagship campuses in Northeastern Philippines. Data were gathered using mathematical connections evaluation, think-aloud strategy, and interviews. Frequency counts, percentages, mean percent scores, Pearson product-moment correlation coefficient, and analysis of the interviews' transcriptions were employed in the study. Results showed that the prospective teachers performed best in making instruction-oriented connections but have difficulty in making implications connection. Also, the prospective teachers' ability to successfully make the mathematical connection is directly linked to their knowledge of the concept, the algorithm, and the part-whole relationship. As such, reform efforts should be made to enhance mathematical connections, emphasizing statistical thinking, and reasoning. Moreover, higher education institutions should incorporate connecting as one of the intended learning outcomes for prospective mathematics teachers

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

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

PhenoRice:A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

Agricultural monitoring systems require spatio-temporal information on widely cultivated staple crops like rice. More emphasis has been made on area estimation and crop detection than on the temporal aspects of crop cultivation, but seasonal and temporal information such as i) crop duration, ii) date of crop establishment and iii) cropping intensity are as important as area for understanding crop production. Rice cropping systems are diverse because genetic, environmental and management factors (G × E × M combinations) influence the spatio-temporal patterns of cultivation. We present a rule based algorithm called PhenoRice for automatic extraction of temporal information on the rice crop using moderate resolution hypertemporal optical imagery from MODIS. Performance of PhenoRice against spatially and temporally explicit reference information was tested in three diverse sites: rice-fallow (Italy), rice-other crop (India) and rice-rice (Philippines) systems. Regional product accuracy assessments showed that PhenoRice made a conservative, spatially representative and robust detection of rice cultivation in all sites (r2 between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r2 = 0.98, Mean Error = 4.07 days, Mean Absolute Error = 9.95 days, p < 0.01). Variability in algorithm performance in different conditions in each site (irrigated vs rainfed, direct seeding vs transplanting, fragmented vs clustered rice landscapes and the impact of cloud contamination) was analysed and discussed. Analysis of the maps revealed that cropping intensity and season length per site matched well with local information on agro-practices and cultivated varieties. The results show that PhenoRice is robust for deriving essential temporal descriptions of rice systems in both temperate and tropical regions at a level of spatial and temporal detail that is suitable for regional crop monitoring on a seasonal basis

Yield gap analysis of field crops: Methods and case studies

The challenges of global agriculture have been analysed exhaustively and the need has been established for sustainable improvement in agricultural production aimed at food security in a context of increasing pressure on natural resources. Whereas the importance of R&D investment in agriculture is increasingly recognised, better allocation of limited funding is essential to improve food production. In this context, the common and often large gap between actual and attainable yield is a critical target. Realistic solutions are required to close yield gaps in both small and large scale cropping systems worldwide; to make progress in this direction, we need (1) definitions and techniques to measure and model yield at different levels (actual, attainable, potential) and different scales in space (field, farm, region, global) and time (short, long term); (2) identification of the causes of gaps between yield levels; (3) management options to reduce the gaps where feasible and (4) policies to favour adoption of gap-closing technologies. The aim of this publication is to review the methods for yield gap analysis, and to use case studies to illustrate different approaches, hence addressing the first of these four requirements. Theoretical, potential, water-limited, and actual yield are defined. Yield gap is the difference between two levels of yield in this series. Depending on the objectives of the study, different yield gaps are relevant. The exploitable yield gap accounts for both the unlikely alignment of all factors required for achievement of potential or water limited yield and the economic, management and environmental constraints that preclude, for example, the use of fertiliser rates that maximise yield, when growers’ aim is often a compromise between maximising profit and minimising risk at the whole-farm scale, rather than maximising yield of individual crops. The gap between potential and water limited yield is an indication of yield gap that can be removed with irrigation. Spatial and temporal scales for the determination of yield gaps are discussed. Spatially, yield gaps have been quantified at levels of field, region, national or mega-environment and globally. Remote sensing techniques describes the spatial variability of crop yield, even up to individual plots. Time scales can be defined in order to either remove or capture the dynamic components of the environment (soil, climate, biotic components of ecosystems) and technology. Criteria to define scales in both space and time need to be made explicit, and should be consistent with the objectives of the analysis. Satellite measurements can complement in situ measurements. The accuracy of estimating yield gaps is determined by the weakest link, which in many cases is good quality, sub-national scale data on actual yields that farmers achieve. In addition, calculation and interpretation of yield gaps requires reliable weather data, additional agronomic information and transparent assumptions. The main types of methods used in yield benchmarking and gap analysis are outlined using selected case studies. The diversity of benchmarking methods outlined in this publication reflects the diversity of spatial and temporal scales, the questions asked, and the resources available to answer them. We grouped methods in four broad approaches. Approach 1 compares actual yield with the best yield achieved in comparable environmental conditions, e.g. between neighbours with similar topography and soils. Comparisons of this type are spatially constrained by definition, and are an approximation to the gap between actual and attainable yield. With minimum input and greatest simplicity, this allows for limited but useful benchmarks; yield gaps can be primarily attributed to differences in management. This approach can be biased, however, where best management practices are not feasible; modelled yields provide more relevant benchmarks in these cases. Approach 2 is a variation of approach 1, i.e. it is based on comparisons of actual yield, but instead of a single yield benchmark, yield is expressed as a function of one or few environmental drivers in simple models. In common with Approach 1, these methods do not necessarily capture best management practices. The French and Schultz model is the archetype in this approach; this method plots actual yield against seasonal water use, fits a boundary function representing the best yield for a given water use, and calculates yield gaps as the departure between actual yields and the boundary function. A boundary model fitted to the data provides a scaled benchmark, thus partially accounting for seasonal conditions. Boundary functions can be estimated with different statistical methods but it is recommended that the shape and parameters of boundary functions are also assessed on the basis of their biophysical meaning. Variants of this approach use nitrogen uptake or soil properties instead of water. Approach 3 is based on modelling which may range from simple climatic indices to models of intermediate (e.g. AquaCrop) or high complexity (e.g. CERES-type models). More complex models are valuable agronomically because they capture some genetic features of the specific cultivar, and the critical interaction between water and nitrogen. On the other hand, more complex models have requirements of parameters and inputs that are not always available. “Best practice” approaches to model yield in gap analysis are outlined. Importantly, models to estimate potential yield require parameters that capture the physiology of unstressed crops. Approach 4 benchmarking involves a range of approaches combining actual data, remote sensing, GIS and models of varying complexity. This approach is important for benchmarking at and above the regional scale. At these large scales, particular attention needs to be paid to weather data used in modelling yield because significant bias can accrue from inappropriate data sources. Studies that have used gridded weather databases to simulate potential and water-limited yields for a grid are rarely validated against simulated yields based on actual weather station data from locations within the same grid. This should be standard practice, particularly where global scale yield gaps are used for policy decisions or investment in R&D. Alternatively, point-based simulations of potential and water-limited yields, complemented with an appropriate up-scaling method, may be more appropriate for large scale yield gap analysis. Remote sensing applied to yield gap analysis has improved over the last years, mainly through pixel-based biomass production models. Site-specific yield validation, disaggregated in biomass radiation-use-efficiency and harvest index, remains necessary and need to be carried out every 5 to 10 years

Global Rice Atlas: Disaggregated seasonal crop calendar and production

Purpose: Rice is an important staple crop cultivated in more than 163 million ha globally. Although information on the distribution of global rice production is available by country and, at times, at subnational level, information on its distribution within a year is often lacking in different rice growing regions. Knowing when and where rice is planted and harvested and the associated production is crucial to policy and decision making on food security. To examine seasonal and geographic variations in food supply, we developed a detailed rice crop calendar and linked it with disaggregated production data. Approach and methods used: We compiled from various sources detailed data on rice production, and planting and harvesting dates by growing season. To standardize the production data to the same period, we adjusted the production values so that the totals for each country will be the same as those of FAO for 2010-2012. We then linked data on rice production with the corresponding crop calendar information to estimate production at harvest time by month then we calculated totals for each country and region. Key results: The bulk of global annual harvests of rice is from September to November, corresponding with the harvest of the wet season rice in Asia and Africa. Total rough rice production during those peak months exceed 381 million tons, which account for about half of annual global rice output. Production is lowest in January with only 11 million tons in total. Regional production is lowest in Asia in January, Americas in December, Africa in July and rest of the world in May. Synthesis and Applications: A globally complete and spatially detailed rice crop calendar is important to crop growth simulation modelling and assessment of vulnerability of rice areas to biotic and abiotic stresses. Linked to production estimates, it can be used in analyzing spatial and seasonal production trends to better assess and predict price fluctuations , and to mitigate potential significant shortfalls in food production at certain times of the year

Monitoring, reporting, and verification system for rice production aligned with Paris Agreement transparency guidelines

This critical review takes a novel approach to assessing the existing Monitoring, Reporting and Verification (MRV) methodology and tools and provides expert-based recommendations for adjusted MRV standards that adapt current guidelines as a promising way forward to deliver transparency in meeting the Nationally Determined Contributions of Vietnam. Additionally, this is a timely proposition given the necessity to define an MRV framework for NAMAs for the rice sector. We are recommending a multi-pronged approach using several tools that can support and validate each other to achieve a robust mechanism for MRV estimations in the rice sector. Examples from the country will be used as a case study given their government’s strong commitment to mitigation in the rice sector

Methane emissions from global rice fields: Magnitude, spatiotemporal patterns, and environmental controls

Given the importance of the potential positive feedback between methane (CH4) emissions and climate change, it is critical to accurately estimate the magnitude and spatiotemporal patterns of CH4 emissions from global rice fields and better understand the underlying determinants governing the emissions. Here we used a coupled biogeochemical model in combination with satellite‐derived contemporary inundation area to quantify the magnitude and spatiotemporal variation of CH4 emissions from global rice fields and attribute the environmental controls of CH4 emissions during 1901–2010. Our study estimated that CH4 emissions from global rice fields varied from 18.3 ± 0.1 Tg CH4/yr (Avg. ±1 SD) under intermittent irrigation to 38.8 ± 1.0 Tg CH4/yr under continuous flooding in the 2000s, indicating that the magnitude of CH4 emissions from global rice fields is largely dependent on different water schemes. Over the past 110 years, our simulated results showed that global CH4 emissions from rice cultivation increased by 85%. The expansion of rice fields was the dominant factor for the increasing trends of CH4 emissions, followed by elevated CO2 concentration, and nitrogen fertilizer use. On the contrary, climate variability had reduced the cumulative CH4 emissions for most of the years over the study period. Our results imply that CH4 emissions from global rice fields could be reduced through optimizing irrigation practices. Therefore, the future magnitude of CH4 emissions from rice fields will be determined by the human demand for rice production as well as the implementation of optimized water management practices

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Context Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used

Mapping the suitability of selected crops in the Ganges Delta

Assessing the suitability of different crops in specific geographic locations is crucial for optimizing crop productivity, promoting climate resilience, and guiding land use decisions. This study assessed the biophysical suitability of rice, watermelon and maize in the Ganges Delta, one of the most densely populated deltas in the world and also extremely vulnerable to climate change. This delta is expected to increasingly experience more frequent and intense extreme weather events, sea level rise and food insecurity. The suitability maps could be used in targeting alternative cropping systems, adjusting crop calendar and recommending crop management practices to increase productivity and improve the resilience of the Ganges Delta