Specialty crops, like flowers, herbs, and vegetables, generally do not have an adequate spectrum of herbicide chemistries to control weeds and have been dependent on hand weeding to achieve ...commercially acceptable weed control. However, labor shortages have led to higher costs for hand weeding. There is a need to develop labor-saving technologies for weed control in specialty crops if production costs are to be contained. Machine vision technology, together with data processors, have been developed to enable commercial machines to recognize crop row patterns and control automated devices that perform tasks such as removal of intrarow weeds, as well as to thin crops to desired stands. The commercial machine vision systems depend upon a size difference between the crops and weeds and/or the regular crop row pattern to enable the system to recognize crop plants and control surrounding weeds. However, where weeds are large or the weed population is very dense, then current machine vision systems cannot effectively differentiate weeds from crops. Commercially available automated weeders and thinners today depend upon cultivators or directed sprayers to control weeds. Weed control actuators on future models may use abrasion with sand blown in an air stream or heating with flaming devices to kill weeds. Future weed control strategies will likely require adaptation of the crops to automated weed removal equipment. One example would be changes in crop row patterns and spacing to facilitate cultivation in two directions. Chemical company consolidation continues to reduce the number of companies searching for new herbicides; increasing costs to develop new herbicides and price competition from existing products suggest that the downward trend in new herbicide development will continue. In contrast, automated weed removal equipment continues to improve and become more effective.
A satellite-based vegetation index model that tracks daily crop growth and evapotranspiration (ETc) is developed, tested, and validated over irrigated farms in Yuma irrigation districts of Arizona ...and California. Model inputs are remotely sensed normalized difference vegetation index (NDVI) images, crop type maps, and local weather. The utility and novelty of the model is a more accurate assessment of ETc than currently provided by the US Bureau of Reclamation’s evapotranspiration modeling system. The model analyzes NDVI time series data from the European Space Agency’s Sentinel-2 satellites using the Google Earth Engine, constructs FAO-56 style crop growth stages from NDVI, and then estimates daily ETc using pre-defined crop coefficients (Kc) and grass reference evapotranspiration (ETos). Four crops were selected to test and evaluate model performance: short-season broccoli, mid-season cotton and wheat, and perennial alfalfa. Comparison of model results showed that Reclamation reports overestimate alfalfa and wheat ETc by 21–25%, cotton ETc by 6%, and underestimate broccoli ETc by 21%. Variability resolved by the model ranged 6–18% of median total ETc. Comparison of model results with those obtained from 13 eddy covariance sites showed validation discrepancies ranging 1–14%: average total actual ETc differences were 12, − 14, 78, and 87 mm/season, respectively, for alfalfa, broccoli, cotton, and wheat. The wide availability of Sentinel-2 data, collected every 5 days or less, and the rapid processing via Google Earth Engine make the vegetation index model implementation fast and practical. Its accuracy and ability to resolve ETc for every field would benefit the Reclamation water accounting system and provide valuable consumptive water use data for any Colorado River stakeholder.
Abstract Lettuce ( Lactuca sativa L.) is a high-value crop for irrigation districts in the low deserts of the USA Southwest. To ensure maximal crop quality, negligible soil salinity stress, minimal ...nutrient loss and reduced pathogen susceptibility, lettuce irrigation must meet, but not exceed, crop water use requirements. However, lettuce crop water use information is outdated in this region: prior studies were conducted at least four decades ago (1960–1980) and do not represent current varieties, management practices, and climate. To address this shortcoming, 12 commercial sites in Yuma, Arizona, USA were evaluated between 2016 and 2020 to update lettuce water use requirements and crop coefficients. The study measured crop evapotranspiration (ET c ) using eddy covariance observations at eight iceberg and four romaine sites, where planting dates varied throughout the fall. Observed ET c and remote sensing data were used to model the daily soil water balance and derive crop coefficients: single (K c ), basal (K cb ), and soil evaporation (K e ). The analysis was supported by lettuce crop height estimates and fractional vegetative cover (f c ) via remote sensing. Days to maturity averaged 75 ± 15 and 89 ± 12 days for romaine and iceberg, respectively, where season lengths increased as planting dates progressed from early fall to late winter. Average planting date for romaine sites was about 20 days earlier than average iceberg sites. When growing intervals are cast in heat units, dependence on crop type and time of planting was reduced. Average cumulative growing-degree-day and enhanced-degree-day metrics were 1133 ± 87 and 754 ± 48 °C-days, respectively. Seasonal lettuce ET c averaged 278 ± 24 mm. Cumulative irrigation applied, plus precipitation, averaged 355 ± 88 mm. Lettuce K c for sites varied from 0.90 ± 0.13 to 1.19 ± 0.11 and K cb from 0.20 ± 0.05 to 1.01 ± 0.11 for the initial and mid-season growth stages, respectively. These updates will help growers improve their irrigation efficiency for lettuce and provide important documentation needed by water managers.
•NDVI from satellites used to estimate basal crop coefficients in wheat fields.•Actual ET with eddy covariance was compared with modeled ET from remote sensing.•Remotely sensed NDVI accurately ...estimates cumulative, mid-season and late season ET.•Water stress event detected with NDVI.
A three-year study was conducted to assess the ability of satellite-based vegetation index (VI) images to track evapotranspiration over wheat. While the ability of using VIs, notably with the Normalized Difference Vegetation Index (NDVI), to track vegetation growth has been well established, the operational capability to accurately estimate the crop coefficient (Kc) and crop evapotranspiration (ETc) at farm-scale from spaceborne platforms has not been widely studied. The study evaluated wheat ET over 7 sites between 2016 and 2019 in Yuma and Maricopa, Arizona, USA estimated by using Sentinel 2 and Venus satellites to map NDVI time-series for entire wheat cropping seasons, December to June. The basal crop coefficient (Kcb) was modeled by the NDVI time-series and the daily FAO56 reference ETo was obtained by near-by weather network stations. Eddy covariance (EC) stations in each field observed ETc during the same seasonal periods, and applied irrigation amounts were logged. The experiment found that remote sensing of NDVI and modeled Kcb accurately estimated Kc and crop ET during mid-season through senescence in most cases. However, NDVI-based estimation performed less well during early season (<60 days after planting), when observed ETc was highly variable due to frequent rain and irrigation at low crop cover. Mid-season Kc values observed for the seven wheat fields were from 0.92 to 1.14, and end of season Kc values ranged from about 0.20 to 0.40, in close agreement to values reported elsewhere. Seasonal VI-based transpiration and ETc values ranged from 467 to 618 mm, closely agreeing with seasonal EC data, which ranged 499–684 mm. Using the Venus sensor, the study in Maricopa in 2019 revealed that when augmented by a background soil water balance model, water stressed wheat can be detected mid-season with NDVI. This capability is specifically due to the sensor’s ability to provide well-calibrated images every 2 days. Findings from this study will help farmers, irrigators, and water managers use and understand the capabilities of visible near infrared remote sensing to track ETc from space.
Winter vegetables, including lettuce, are a significant consumptive use of water in the Lower Colorado River Basin. Precise irrigation management is needed to increase water use efficiency and reduce ...the negative impacts of suboptimal irrigation, including nutrient leaching, crop stress, and crop pathogens. However, lettuce has multiple features that make accurate evapotranspiration (ET) modeling difficult, including asynchronicity with meteorological evaporative demand, short growing seasons, and a shallow root zone that increases the risk of using an incorrect ET value. To improve ET modeling and understand applied irrigation effectiveness for lettuce in this region, we used an energy and water balance bio-physical model, Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution (BAITSSS) on arid farmlands in the lower Colorado River basin. The study was conducted between 2016 and 2020 at twelve eddy covariance (EC) sites in lettuce with a wide range of soil physical properties. BAITSSS was implemented using ground-based weather and irrigation data, and remote sensing-based vegetation indices (Sentinel-2). The model accuracy varied among sites, with a mean cumulative seasonal ET of ~ 3% and mean RMSE of 1.1 mm d
−1
when compared to EC. The results showed that accurate timing and amount of applied water (irrigation and precipitation) were critical to capturing ET spikes right after irrigation and tracking the continuous decrease of ET. This study highlighted the dominant factors that influence the ET of lettuce and how BAITSSS can improve ET modeling for irrigation management.
Efficient irrigation is critical for managing scarce water resources where precipitation is minimal. Field-scale irrigation is largely unaccounted for in landscape evapotranspiration models, ...primarily due to the unavailability of data and the lack of water balance components in energy balance-based evapotranspiration models. To overcome these challenges, we implemented a remote sensing-based energy and water balance model BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature Solution) to calculate evapotranspiration (ET) and irrigation requirements of winter lettuce in the arid environment of the Lower Colorado River Basin. Predicted evapotranspiration and irrigation were compared against data from twelve eddy covariance (EC) sites for wide range of soil hydraulic properties operating between 2016 and 2020 and the applied irrigation, respectively. BAITSSS estimated evapotranspiration and irrigation based on vegetative formation, weather demand, soil hydraulic characteristics, and predefined management allowed depletion (MAD) (0.4–0.6). Ground-based weather data, Sentinel-2-based vegetation indices, and SSURGO (NRCS soil survey database) soil moisture characteristics were model inputs. The results showed mean seasonal ET from BAITSSS and EC were comparable, differing on average by about 7% based on a constant rooting depth (500 mm) and MAD of 0.5 for entire crop growth stages. Variations in daily and seasonal ET were mainly due to differences in applied and model-simulated irrigation. Seasonal values of applied and simulated irrigation closely agreed (~ 6%) in most sites, though some sites applied irrigation more effectively than others. Overall, this study provided insight into consumptive water use and field-scale irrigation practices, as well as the capabilities and limitations of model-simulated ET and irrigation.
Water shortages in the Western United States will continue to be one of the foremost American agricultural challenges in the coming years. As agriculture is the largest consumer of water in the ...western US, improvements in irrigation scheduling and modeling are needed to maximize production under limited water. Various satellite-based remote sensing models have been developed to estimate crop water use. However, water balance-based evapotranspiration (ET) models need field-scale irrigation information for initiating the seasonal soil water balance. This initialization has been challenging due to the lack of remotely sensed irrigation event data. In this study, we utilized a recently launched satellite constellation (Planet) with high temporal and spatial resolution data (daily, ~ 3 m) to evaluate if Planet data can facilitate early season irrigation detection. We utilized normalized difference vegetation index (NDVI), moisture index, and individual spectral bands to detect moisture and ultimately infer irrigation. As part of this comparison, a hybrid two-source energy and water balance model BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution) was used to estimate ET with Planet-based vegetation indices and irrigation information. We also compared the results to eddy covariance (EC) located at lettuce fields in Yuma, Arizona in the lower Colorado River basin between 2016 and 2020. Overall, the results indicated that Planet’s data helped to establish the field-scale onset of irrigation, which assisted to initiate soil water balance in the BAITSSS model, thus ultimately improving ET. Further, these results should support the development of near-real-time landscape-scale ET and should be highly beneficial to agricultural communities for sub-field-scale effective water management.
The lack of molecular diagnosis in the field of cancer in Iraq has motivated us to perform a genetic analysis of pediatric acute myelogenous leukemia (AML), including class I and II aberrations. ...Peripheral blood or bone marrow cells were collected from 134 AML children aged ≤15 years. Flinders Technology Associates (FTA) filter paper cards were used to transfer dried blood samples from five Iraqi hospitals to Japan. DNA sequencing was performed to identify class I mutations. Nested RT-PCR was used to detect class II aberrations, except that
MLL
rearrangement was detected according to long distance inverse-PCR.
NPM1
and FMS-like tyrosine kinase 3-internal tandem duplication (
FLT3
-ITD) mutations were analyzed by GeneScan using DNA template. Among 134 Iraqi pediatric AML samples, the most prevalent FAB subtype was M2 (33.6 %) followed by M3 (17.9 %). Class I mutations: 20 (14.9 %), 8 (6.0 %), and 8 (6.0 %) patients had
FLT3
-ITD,
FLT3
-TKD, and
KIT
mutations, respectively. Class II mutations: 24 (17.9 %), 19 (14.2 %), and 9 (6.7 %) children had
PML
-
RARA
,
RUNX1
-
RUNX1T1
, and
CBFB
-
MYH11
transcripts, respectively.
MLL
rearrangements were detected in 25 (18.7 %) patients.
NPM1
mutation was detected in seven (5.2 %) cases. Collectively, approximately 30 % of AML children were proved to carry favorable prognostic genetic abnormalities, whereas approximately 10 % had high
FLT3
-ITD allelic burden and needed a special treatment plan including allogeneic hematopoietic stem cell transplantation. Acute promyelocytic leukemia (APL) was frequent among Iraqi pediatric AML. It is likely that molecular diagnosis using FTA cards in underdeveloped countries could guide doctors towards an appropriate treatment strategy.
