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Abstract Vegetation health forecasting (NDVI as a proxy) informs decision‐makers about the end of season crop yield productivity but is not well‐documented. This study tests improvements in vegetation health forecasting by developing a data‐driven Dynamic Agricultural Productivity Indicator (DAPI), which simultaneously incorporates satellite‐based root zone soil moisture (RZSM) and satellite‐based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in the CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology‐based NDVI forecasts, with the largest improvements in water‐limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food‐insecure areas, predicting crop price changes, and projecting expected commodities market trends.

This study aimed to explore the interaction between growth rate on specific injury incidence and burden on pre-, circa- and post-peak height velocity (PHV) periods. Injury and stature data collected during the 2000–2020 seasons in an elite football academy were retrospectively analysed. Only players with height measurements from childhood until the attainment of adult height were included in the study (N = 84). Growth data were smoothed using a cubic spline to calculate daily growth rate and height. Growth rate was categorised into three groups: fast (> 7.2 cm/year), moderate (3.5–7.2 cm/year) and slow (< 3.5 cm/year). Percentage of observed adult height was used to classify players as pre-PHV ( 95%). Overall and specific injury incidence and burden and rate ratios for comparisons between growth rate groups were calculated on pre-, circa- and post-PHV periods, separately. Overall injury incidence and burden were greater in pre-PHV players with quicker growth rates compared to players growing moderately and slowly. All in all, players with more rapid growth-rates were at higher risk for growth-related injuries in all pre-, circa- and post-PHV periods. Post-PHV, the incidence and burden of joint/ligament injuries were 2.4 and 2.6-times greater in players growing slowly compared to players growing moderately. Practitioners should monitor growth rate and maturity status and consider their interaction to facilitate the design of targeted injury risk reduction strategies.

Annonaceae is a major pantropical family with 113 genera and about 2550 species. Cameroon is one of the most biodiverse countries in Africa but its flora remains incompletely known. In this volume of the Flora of Cameroon, we describe 166 native taxa representing 163 species in 28 native genera within the family Annonaceae. A total of 22 species (about 13%) are endemic to the country. We provide keys to all native genera, species, and infraspecific taxa. For each species a detailed morphological description and a map of its distribution in Cameroon are provided. Distribution maps and diversity analyses are based on a taxonomically verified database of 2073 collections. Across Africa, Cameroon is a center of diversity for Annonaceae harboring one of the highest numbers of species and genera. For example, Cameroon harbors the highest number of African species for the only pantropical genus of Annonaceae, Xylopia. Annonaceae are found across all 10 administrative regions of Cameroon but diversity is concentrated within the tropical rain forest areas situated in the south and South-West. The areas around Bipindi and Mount Cameroon show the highest levels of diversity, but this is correlated with collection effort. Line drawings and/or photographs accompany most species. One species new to science Uvariopsis etugeana Dagallier & Couvreur sp. nov. is described. We also undertake a number of nomenclatural changes such as lectotypifications, six new synonymies and two new combinations ( Uvaria anisotricha (Le Thomas) Couvreur, comb. nov.; Uvariodendron fuscum var. giganteum (Engl.) Dagallier & Couvreur, comb. nov.).

During botanical expeditions focused on the study of Annonaceae in Thailand, specimens resembling Friesodielsia glauca were collected from the lower part of peninsular Thailand but were believed to differ from that species in vegetative, flower, and fruit characters. A morphological investigation of the new specimens and species complex was conducted. Specimens (including types of F. glauca and similar species collected from Thailand and adjacent regions) were examined in the following herbaria: A, BK, BM, BKF, E, K, KEP, KKU, L, PSU, QBG, SING, and US. As a consequence, the new species Friesodielsia parvimitra, endemic to peninsular Thailand, is described and illustrated. The conservation status of the new species was provisionally assessed as Critically Endangered (CR, B2ab(iii)) following the IUCN’s guidelines. In addition, F. argentea, previously regarded F. glauca, is reinstated here as a distinct species based on several morphological differences and is reported as a new record from Thailand. Photographs, line drawings, and a revised key to F. glauca and similar species are presented.

Background Neuroendovascular practices are transitioning from transfemoral to a radial‐first approach for cerebral angiography. Developing radial‐specific devices requires a comprehensive understanding of vessel size and geometry encountered from a right radial approach. We developed a database of aortic arch and vessel measurements relevant for radial‐specific device development. Methods The database contained 100 consecutive neck computed tomographic angiograms conducted on a 256‐slice emergency department computed tomographic scanner. Arch characteristics and measurements obtained from 1‐cm maximum intensity projection reformats included arch type and diameter, bifurcation angles, and distances from the arch to vessel origins. Statistical analysis was conducted using Excel and R. Results The database contained 56 female and 44 male patients; the average age was 62 years. A total of 46% had type 2 or 3 arch configuration, 19% had bovine configuration, and 5% had a left vertebral artery originating directly from the arch. Median angles at the arch from the innominate ranged from 20° to 47° and at branch vessels from 16° to 63° with large variation. The left carotid bifurcation was ≈8 cm further than the right carotid bifurcation. The left vertebral artery origin was median 3.8 cm from the arch. Conclusions We cataloged aortic arch and vessel geometries that have implications for catheter and device performance from a transradial approach. For example, a standard guide catheter that is adequate length for the right anterior circulation must be at least 8 cm longer for comparable distal support on the left because of the difference in distance from the right to the left. Catheter tip shape may be further optimized to improve vessel selection. Currently used catheters make a reverse curve angle of ≈15°, although most great vessels are at between 15° and 45°. Finally, the length of the distal arm of reverse curve catheters is just >4 cm, frequently inadequate to easily reach the left vertebral artery origin (median distance, 3.8 cm in this study). Transradial devices should be developed with these constraints in mind. This database provides benchmarks for future catheter design.

The generic status of Winitia Chaowasku (Annonaceae Juss., Miliuseae Hook.f. & Thomson) is reaffirmed by an extensive phylogenetic reconstruction using seven plastome regions (matK, ndhF, rbcL, ycf1 exons; trnL intron; psbA-trnH, trnL-trnF intergenic spacers) and including, among others, seven accessions of Winitia plus two accessions of its sister group, Stelechocarpus Hook.f. & Thomson. The results disclosed a maximally supported clade of Winitia, as well as of Stelechocarpus. The sister relationship of the two genera is still rather poorly supported and the branch uniting them is very short, whereas the branches leading to Winitia and to Stelechocarpus are relatively long, corresponding to their considerable morphological differences. Additionally, in Miliuseae there is a particular indel of eight continuous base pairs in the trnL-trnF intergenic spacer potentially diagnostic for generic discrimination, i.e., members in the same genus possess the same indel structure (absence or presence of a gap), and this indel differentiates Winitia from Stelechocarpus. Winitia cauliflora (Scheff.) Chaowasku appears polyphyletic and Winitia longipes (Craib) Chaowasku & Aongyong comb. nov. based on Stelechocarpus longipes Craib is consequently proposed. Furthermore, our phylogenetic data support a new species, Winitia thailandana Chaowasku & Aongyong sp. nov. from southern Thailand, which is described and illustrated. A key to genera in the sageraeoid clade (Sageraea-Winitia-Stelechocarpus) and a key to the four species of Winitia are provided.

A revision of the 45 species of the pantropical genus Xylopia in Tropical Africa includes descriptions of six new species and a new section of the genus. The fruits and seeds of Xylopia show specializations that promote vertebrate dispersal, primarily by hornbills and monkeys. Over half of the African species have an Area of Occupancy (AOO) less than 80 km2, suggesting that they are in need of protection. African species are classified into five sections. Section Neoxylopia , with four species, is centered in the Guineo-Congolian Region and includes X. globosa sp. nov. Section Ancistropetala, with three species, occurs in the same region. Both of these sections are endemic to Africa. Section Xylopia, which extends to Madagascar and the American tropics, has only a single species in Africa, X. aethiopica. The three species of section Verdcourtia sect. nov. are restricted to the East African coast and Madagascar. The largest number of African species, (34) belong to section Stenoxylopia, in which the seeds lack the arils found in the other sections and instead have a fleshy sarcotesta. Section Stenoxylopia is divided into two informal groups, one centered in eastern and southern Africa (X. odoratissima group) and the other centered in the wetter forests of western and central Africa (X. acutiflora group). Five new species are described in section Stenoxylopia: Xylopia nilotica sp. nov. from Sudan, South Sudan, and Uganda, Xylopia calva sp. nov. from Nigeria and Cameroon, which is allied to X. phloiodora, and Xylopia monticola sp. nov. from Nigeria and Cameroon, X. piratae sp. nov. from Ivory Coast and Ghana, and X. unguiculata sp. nov. from Gabon. The latter three species are segregates of the former Xylopia acutiflora s. l. One new combination is made at the species level, X. shirensis comb. nov. Keys, descriptions, illustrations, distribution maps, and an index to numbered collections document diversity and assist with species identification. The name Unona oliveriana Baill. was found to pre-date the name Unona lepidota Oliv., requiring the combination Meiocarpidium oliverianum comb. nov.

Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.