Winter Wheat Plots Research Articles

Limitations and solutions for developing a grain yield and protein content forecasting model based on vegetation indices in organic wheat production – on-farm experimentation

ABSTRACT Multispectral imaging has gained increased attention in recent years for monitoring and predicting crop-related characteristics. Vegetation indices can provide valuable insight into crop health and growth, especially in conventional agriculture and small-sized experimental plots. However, the effectiveness of such methods in non-conventional agricultural systems still needs to be clarified. This study was the first to test three vegetation indices (NDVI, GNDVI, NDRE) for grain yield and protein content prediction in organic farming under real-life farm management practices. Using two small-plot and one on-farm organic wheat variety trial sites, unmanned aerial vehicle-based remote sensing data was compared with physical sampling results in 2 years to assess the potential of agricultural remote sensing to predict yield and protein content on small- and medium-sized plots of organic winter wheat (Triticum aestivum L.). The correlations between vegetation indices and crop traits were stronger in small- than in medium-sized plots (r max = 0.85 and 0.75, respectively, for grain yield), probably due to increased heterogeneity of the larger plots, due to differences in weed cover, soil nutrient supply, etc., and it was strongly affected by the time of measurement. Linear equation for yield and protein content prediction was not sufficiently precise; the machine learning algorithms improved the accuracy of yield estimation. Both methods considered NDRE with a higher weight than NDVI when modelling grain yield and protein content based on the vegetation index values, which suggested that NDRE was more suitable for prediction. Small-plot-based predictions over-estimated grain yield and under-estimated grain protein content in medium-sized plots.

Cross-Modal Segmentation Network for Winter Wheat Mapping in Complex Terrain Using Remote-Sensing Multi-Temporal Images and DEM Data

Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for better agricultural management, land planning, and environmental sustainability. However, the distribution style of winter wheat planting fields is not consistent due to different terrain conditions. In mountainous areas, winter wheat planting units are smaller in size and fragmented in distribution compared to plain areas. Unfortunately, most crop-mapping research based on deep learning ignores the impact of topographic relief on crop distribution and struggles to handle hilly areas effectively. In this paper, we propose a cross-modal segmentation network for winter wheat mapping in complex terrain using remote-sensing multi-temporal images and DEM data. First, we propose a diverse receptive fusion (DRF) module, which applies a deformable receptive field to optical images during the feature fusion process, allowing it to match winter wheat plots of varying scales and a fixed receptive field to the DEM to extract evaluation features at a consistent scale. Second, we developed a distributed weight attention (DWA) module, which can enhance the feature intensity of winter wheat, thereby reducing the omission rate of planting areas, especially for the small-sized regions in hilly terrain. Furthermore, to demonstrate the performance of our model, we conducted extensive experiments and ablation studies on a large-scale dataset in Lanling county, Shandong province, China. Our results show that our proposed CM-Net is effective in mapping winter wheat in complex terrain.

Soil Chemical Properties and Microbial Behavior under Short-Term Organic and Mineral Fertilization within Different Crops

Agronomic practices can have a negative impact on soil health and quality and ecosystem resilience. The objectives of the study were (1) to evaluate the soil chemical properties and microbial abundance under short-term application of organic and mineral fertilizers and different cultivated crops and (2) to observe the antifungal efficacy of microorganisms isolated from the studied soil. A field trial was conducted in the 2021–2022 period on a preluvosoil-type soil in four randomized blocks with eight fertilizer treatments based on manure compost (MC) and MC + mineral fertilizer (V1—control-soil; V2—NPK only; V3—15 t/ha MC; V4—15 t/ha MC + NPK; V5—30 t/ha MC; V6—30 t/ha MC + NPK; V7—60 t/ha MC; and V8—60 t/ha MC +NPK) and four crops (winter wheat—Triticum aestivum L., maize—Zea mays L., soybean—Glycine max L., and a mixture of perennial grasses and legumes). In almost all treatments, the soil pH decreased during the summer–autumn period. The organic carbon (Corg) and humus contents increased compared to the initial state of the soil after the application of different doses of MC and MC + NPK fertilization in almost all treatments. The microbial load of the soil was influenced by the fertilization regime and crop species, but there were no significant differences between the variants. The highest bacterial load was recorded in soil cultivated with a mixture of perennial grasses and grain legumes, i.e., in the variant with 15 t/ha MC, followed by soil cultivated with maize and fertilized with 30 t/ha and 60 t/ha MC. A higher number of fungi was observed in the mixture of perennial grasses and legumes, and Rhizobium population was higher, especially in the winter wheat plots, despite the fertilization regime. The antifungal efficacy of the microorganisms isolated from the samples was medium to low, except in the winter wheat experiment, where the efficacy against Fusarium culmorum was medium to high and against other pathogens was medium. In the other crops within the experiment, the antagonistic activity of the soil microorganisms was medium to low.

Soil microbial communities are sensitive to differences in fertilization intensity in organic and conventional farming systems.

Intensive agriculture has increased global food production, but also impaired ecosystem services and soil biodiversity. Organic fertilization, essential to organic and integrated farming, can provide numerous benefits for soil quality but also compromise the environment by polluting soils and producing greenhouse gases through animal husbandry. The need for reduced stocking density is inevitably accompanied by lower FYM inputs, but little research is available on the impact of these effects on the soil microbiome. We collected soil samples from winter wheat plots of a 42-year-old long-term trial comparing different farming systems receiving farmyard manure at two intensities and measured soil quality parameters and microbial community diversity through DNA metabarcoding. High-input fertilization, corresponding to 1.4 livestock units (LU) improved the soil's nutritional status and increased soil microbial biomass and respiration when compared to low-input at 0.7 LU. Bacterial and fungal α-diversity was largely unaffected by fertilization intensity, whereas their community structure changed consistently, accompanied by an increase in the bacterial copiotroph-to-oligotroph ratio in high-input systems and by more copiotrophic indicator OTUs associated with high than low-input. This study shows that reduced nutrient availability under low-input selects oligotrophic microbes efficiently obtaining nutrients from various carbon sources; a potentially beneficial trait considering future agroecosystems.

Effects of Crop Rotation and Topography on Soil Erosion and Nutrient Loss under Natural Rainfall Conditions on the Chinese Loess Plateau

Erosive rainfall results in the loss of both soil and nutrients, which indirectly triggers soil deterioration and a reduction in land productivity. However, how rainfall affects runoff, soil erosion, and nutrient loss under different crop rotation patterns and topographic factors remains unclear. This experiment observed nine runoff-erosion plots on the Chinese Loess Plateau (CLP) from 2019 to 2020 to determine the effects of crop type, rotation pattern, and slope gradient and length on runoff, soil erosion, and nutrient loss. Runoff, soil erosion, and nutrient loss were highest for the fallow plots; values for these variables for spring corn and winter wheat plots were not significantly different. Crop rotation generated greater runoff, soil erosion, and nutrient loss compared to non-rotation. Soil erosion and associated nutrient loss increased, but not significantly, with slope for gradients of 0.5°, 1°, and 3°, while runoff and associated nutrient loss did not increase. In addition, soil erosion and associated nutrient loss were significantly greater for slope lengths of 20 m vs. 50 m. A structural equation model showed rainfall characteristics significantly impacted runoff and soil erosion and subsequently affected nutrient loss. This study increases the understanding of runoff, soil erosion, and nutrient loss from cropland with gentle slopes on the CLP.

ANALYSIS OF VALUABLE CHARACTERISTICS OF DOMESTIC VARIETIES OF SOFT WINTER WHEAT AS A SOURCE MATERIAL FOR THE SELECTION OF NEW HIGH-YIELDING VARIETIES IN THE CONDITIONS OF THE IVANIVSKA RESEARCH AND SELECTION STATION

The article highlights the results of a study of the breeding value for a number of main economic and valuable traits (plant height, weight of 1000 grains, yield) of twenty-five varieties of soft winter wheat of domestic selection at the collection yard of the Ivanovska research and breeding station of the Institute of Bioenergy Crops and Sugar Beet of the National Academy of Sciences of Ukraine. It is noted that the organization and technology of growing soft winter wheat on the plots of the collection nursery of the Ivanivska research and selection station corresponds to the generally accepted classical methods, which are widely used in breeding practice in the process of creating soft winter wheat plots and in the research case. It was established that all studied varieties of the lutescens type compared to the standard (Pearl of the forest-steppe) have an average of 16% higher plant height, 4% greater mass of 1000 grains and significantly – 44%, higher yield than the standard. It was established that 75% of the studied variety samples of the erythrospermum type compared to the standard (Poliska 90) are more stunted, characterized by 10–20% less mass of 1000 grains, at the same time in terms of yield, 94% of the investigated varietal samples of this variety exceed the standard – on average by almost 24%. It has been shown that the soft winter wheat of the lutescens type – Trudivnytsia Myronivska, Privitna and Pishna, the type of the erythrospermum variety Lyutenka combine low height with high ear productivity and yield, therefore they are valuable raw materials as donors for short stem and high productivity for further selection work aimed at creating new highly adaptive varieties of soft winter wheat with high yield and grain quality in the conditions of the southern forest-steppe of Ukraine. It is shown that practically all studied varieties of soft winter wheat of domestic selection can serve as sources for further selection practice, and can also be used in production crops as promising for the southern Forest Steppe of Ukraine in terms of productivity.

Assessing the Potential of Downscaled Far Red Solar-Induced Chlorophyll Fluorescence from the Canopy to Leaf Level for Drought Monitoring in Winter Wheat

Solar-induced chlorophyll fluorescence (SIF) from ground, airborne, and satellite-based observations has been increasingly used in drought monitoring recently due to its close relationship with photosynthesis. SIF emissions respond rapidly to droughts, relative to the widely used vegetation indices (VIs), thus indicating their potential for early drought monitoring. The response of SIF to droughts can be attributed to the confounding effects of both the physiology and canopy structure. In order to reduce the reabsorption and scattering effects, the total emitted SIF (SIFtot) was proposed and served as a better tool to estimate GPP compared with the top-of-canopy SIF (SIFtoc). However, the response time and response magnitude of SIFtot to droughts and its relationships with the environmental parameters and soil moisture (SM) (i.e., the knowledge of drought monitoring using SIFtot) remains unclear. Here, the continuous ground data of F760toc (SIFtoc at 760 nm) from a nadir view that was downscaled to F760tot (SIFtot at 760 nm), NIRv, and the NDVI, SM, meteorological, and crop growth parameters were measured from four winter wheat plots with different intensities of drought (well-watered, moderate drought, severe drought, and extreme drought) over 2 months. The results indicated that F760tot was more closely correlated with the SM than the VIs at short time lags but weaker at longer time lags. The daily mean values of F760tot and NIRv were able to distinguish the differences between different drought levels, and F760tot responded quickly to the onset of drought, especially for the moderate drought intensity. These findings demonstrated that F760tot has potential for early drought monitoring and may contribute to mitigating the risk of agricultural drought.

Agricultural Production on Erosion-Affected Land from the Perspective of Remote Sensing

In this article, we discuss the influence of soil erosion on crop yield in the erosion-prone chernozem region of South Moravia. Erosional and depositional areas show significant differences in soil properties, which are also reflected in total crop yield. Plots of winter wheat, grown during the years 2016–2019 were used for analysis. The Enhanced Vegetation Index (EVI), referred to in literature as one of the best correlates of yield, was used to provide indirect information on yield. Although erosional areas are visible on orthophoto images on chernozem soils, the necessary orthophoto images are not always available. Thus, we have proposed a method for the identification of such erosion-affected areas based on the use of Sentinel 2 satellite images and NDVI or NBR2 indices. The relationship between yield and erosion was expressed through Pearson’s correlation on a sample of pixels randomly selected on the studied plots. The results showed a statistically significant linear reduction in yield depending on the level of degradation. All plots were further reclassified, according to level of degradation, as high, medium, or low state of degradation, where the average EVI values were subsequently calculated. Yield on non-degraded soil is 16 ± 1% higher on average.

Who is the culprit: Is pest infestation responsible for crop yield losses close to semi-natural habitats?

Semi‐natural habitats (SNHs) are becoming increasingly scarce in modern agricultural landscapes. This may reduce natural ecosystem services such as pest control with its putatively positive effect on crop production. In agreement with other studies, we recently reported wheat yield reductions at field borders which were linked to the type of SNH and the distance to the border. In this experimental landscape‐wide study, we asked whether these yield losses have a biotic origin while analyzing fungal seed and fungal leaf pathogens, herbivory of cereal leaf beetles, and weed cover as hypothesized mediators between SNHs and yield. We established experimental winter wheat plots of a single variety within conventionally managed wheat fields at fixed distances either to a hedgerow or to an in‐field kettle hole. For each plot, we recorded the fungal infection rate on seeds, fungal infection and herbivory rates on leaves, and weed cover. Using several generalized linear mixed‐effects models as well as a structural equation model, we tested the effects of SNHs at a field scale (SNH type and distance to SNH) and at a landscape scale (percentage and diversity of SNHs within a 1000‐m radius). In the dry year of 2016, we detected one putative biotic culprit: Weed cover was negatively associated with yield values at a 1‐m and 5‐m distance from the field border with a SNH. None of the fungal and insect pests, however, significantly affected yield, neither solely nor depending on type of or distance to a SNH. However, the pest groups themselves responded differently to SNH at the field scale and at the landscape scale. Our findings highlight that crop losses at field borders may be caused by biotic culprits; however, their negative impact seems weak and is putatively reduced by conventional farming practices.

Impacts of lime application on soil bacterial microbiome in dryland wheat soil in the Pacific Northwest

Soil acidification is a global issue that significantly reduces crop productivity. Lime application is a viable agricultural strategy to ameliorate soil acidification. However, the knowledge of the impact of lime application on the soil microbiome is still limited. Therefore, the objective of this study was to investigate the diversity, structure, and compositions of the soil bacterial community after liming on wheat fields. We established winter wheat plots in two precipitation zones, Pendleton and Moro in Oregon, facing soil acidity stress, and conducted bacterial microbiome analysis after lime application using Illumina MiSeq. Soil pH increased with increasing rates of lime amendments in the surface soil (0–7.5 cm) but did not significantly change in deeper soil (7.5–15 and 15–22.5 cm). High-throughput sequencing results revealed that liming had marginally effects on soil bacterial communities. A few bacterial families, including Cytophagaceae, Flavobacteriaceae, Intrasporangiaceae, Phyllobacteriaceae, Propionibacteriaceae, Pseudomonadaceae, and Sphingobacteriaceae, were correlated with lime application. In contrast, location and soil depth had a strong effect on soil bacterial communities. In addition, the impacts of liming on soil bacterial diversity and taxa were inconsistent and location- and soil depth-dependent. Furthermore, four nitrifiers were quantitated from all samples by qPCR assay. Ammonium oxidizing bacteria (AOB) and Nitrobacter were most abundant in the topsoil, while ammonia oxidizing archaea (AOA) and Nitrospira were more abundant in deeper soil. Only Nitrospira was significantly influenced by soil pH. Overall, these results suggest that location, soil depth, lime amendments, and their interactions have significant impacts on the soil bacterial community that may influence plant health and crop yields.

Early Detection of Stripe Rust in Winter Wheat Using Deep Residual Neural Networks.

Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.

Long-term tillage effect on with-in season variations in soil conditions and respiration from dryland winter wheat and soybean cropping systems

Soil respiration from agricultural soils is a major anthropogenic source of CO2 to the atmosphere. With-in season emission of soil CO2 from croplands are affected by changes in weather, tillage, plant row spacing, and plant growth stage. Tillage involves physical turning of soils which accelerate residue decomposition and CO2 emission. No-tillage lacks soil disturbance and residues undergo slower decomposition at the surface. In this study, we compared with-in season soil conditions (temperature and moisture) and soil respiration from two major crops (soybean and winter wheat) by making high temporal frequency measurements using automated chambers at half-hourly intervals. The experiment lasted for 179 days. Total number of measurements made from conventional and no-tillage soybean and winter wheat plots were 6480 and 4456, respectively. Average flux after the winter-dormancy period of wheat was 37% higher in tilled soil compared to no-till soil. However, average flux during the soybean growing season was 8% lower in conventional till compared to no-till soil. This differential response of soil respiration in wheat and soybean was primarily due to tillage-induced changes in surface characteristics (residue cover) and soil environmental conditions (soil temperature and soil moisture). Results from this study can help elucidate relationships for modeling and assessment of field-scale soil CO2 emissions from dryland wheat and soybean crops grown in sub-tropics.

Relating Hyperspectral Vegetation Indices with Soil Salinity at Different Depths for the Diagnosis of Winter Wheat Salt Stress

Abundant shallow underground brackish water resources could help in alleviating the shortage of fresh water resources and the crisis concerning agricultural water resources in the North China Plain. Improper brackish water irrigation will increase soil salinity and decrease the final yield due to salt stress affecting the crops. Therefore, it is urgent to develop a practical and low-cost method to monitor the soil salinity of brackish irrigation systems. Remotely sensed spectral vegetation indices (SVIs) of crops are promising proxies for indicating the salinity of the surface soil layer. However, there is still a challenge concerning quantitatively correlating SVIs with the salinity of deeper soil layers, in which crop roots are mainly distributed. In this study, a field experiment was conducted to investigate the relationship between SVIs and salinity measurements at four soil depths within six winter wheat plots irrigated using three salinity levels at the Yucheng Comprehensive Experimental Station of the Chinese Academy of Sciences during 2017–2019. The hyperspectral reflectance was measured during the grain-filling stage of winter wheat, since it is more sensitive to soil salinity during this period. The SVIs derived from the observed hyperspectral data of winter wheat were compared with the salinity at four soil depths. The results showed that the optimized SVIs, involving soil salt-sensitive blue, red-edge, and near-infrared wavebands, performed better when retrieving the soil salinity (R2 ≥ 0.58, root mean square error (RMSE) ≤ 0.62 g/L), especially at the 30-cm depth (R2 = 0.81, RMSE = 0.36 g/L). For practical applications, linear or quadratic models based on the screened SVIs in the form of normalized differential vegetation indices (NDVIs) could be used to retrieve soil salinity (R2 ≥ 0.63, RMSE ≤ 0.62 g/L) at all soil depths and then diagnose salt stress in winter wheat. This could provide a practical technique for evaluating regional brackish water irrigation systems.

Effects of acidified pig slurry application on soil chemical and microbiological properties under field trial conditions

Acidification of animal slurry is recommended to reduce NH3 emissions, but little is known about how such slurry influences soil microorganisms. Here we test the impact of acidified pig slurry, untreated slurry, and mineral N–S fertiliser on soil dehydrogenase activity (DHA), microbial community composition (PLFA), soil pH, and SO4-S, NO3-N, and NH4-N content in the 0–20 cm layer in winter wheat (Triticum aestivum L.) plots in Estonia in a field experiment conducted in 2017 and 2018. The slurry was acidified with H2SO4 (96%) to reach the target pH of 6.0 (to imitate the in-storage acidification system). The soil was sampled twice each year: 2 weeks after slurry spreading and at winter wheat harvest. In both years, the spreading of acidified slurry significantly increased the SO4-S content in the soil. The level of SO4-S was remarkably higher 2 weeks after slurry spreading, then decreased towards harvest, but remained significantly higher compared to other treatments. Compared to the untreated slurry, the use of acidified slurry had no significant (p < 0.05) effect on soil pH or NO3-N, and NH4-N content, nor soil DHA and microbial community composition. Only the relative abundance of Actinobacteria was significantly different between treatments, which is likely due do the added nitrogen in the NO3-N.

Effects of Warming and Straw Application on Soil Microbial Biomass Carbon and Nitrogen and Bacterial Community Structure

In order to investigate the effects of warming and straw application on soil microbial biomass carbon and nitrogen and bacterial community structure, a randomized block experiment was performed. Four treatments were included, namely a control (CK), warming (WA), straw application (SA), and warming and straw application (WS) treatments. The soils were sampled during the soybean (Sep. 23, 2017) and winter wheat (April 21, 2018) growing seasons. The soils were used to determine the microbial biomass C and N content using chloroform fumigation methods, and the bacterial community structure was evaluated using high-throughput sequencing (Illumina HiSeq). Results indicated that there was no significant difference in microbial biomass C between different warming and straw application treatments (P>0.05). The microbial biomass N of the warming treatment was significantly higher than that of control in the soybean field (P<0.01). There were significant differences in the most dominant soil bacteria between treatments in the soybean growing season (P<0.05) at the class, order, family, and genus levels, while there was no difference in the winter wheat growing season. The percentages of dominant Gemmatimonadales, Gemmatimonadaceae, and Sphingomonas in the CK and WA (or SA) treatments were significantly different (P<0.05) in the soybean growing season. There was a significant (P<0.05) difference in the dominant Gammaproteobacteria between the CK and WA treatments in the winter wheat growing season. The observed number of species, Shannon index, Simpson index, and Chao1 index were lowest in the warming plots in the soybean growing season and highest in the warming and straw application plots in the winter wheat growing season. The Shannon index for the WA plots was significantly higher than in the WS plots in the soybean growing season (P<0.05). The observed number of species, Shannon index, Simpson index, and Chao1 index were significantly higher in the soybean plots than in the winter wheat plots (P<0.05), while the abundance was significantly higher in the winter wheat plots than in the soybean plots (P<0.05). The soybean growing season had significantly higher diversity than the winter wheat growing season. The indexes of α diversity were highly significantly correlated with soil microbial biomass C and N in the soybean growing season (P<0.001), while there was no such correlation in the winter wheat growing season. The indices of α diversity were significantly correlated in both the soybean growing season and winter wheat growing season (P<0.05).

Characterising dryland salinity in three dimensions

Due to frequent salt migration and large spatial variability within soil profiles, salinity characterisation by traditional drilling sampling methods is time-consuming and labour-intensive. Thus, it is necessary to develop monitoring technology and three-dimensional (3D) characterisation methods for rapid, non-invasive, and accurate soil salinity measurement. This study presents a new framework combining sensor technology and an inversion algorithm to characterise 3D soil salinity. Four typical land-use types (natural desert, natural vegetation, apple orchard, and winter wheat farmland) in the Aksu region of southern Xinjiang were surveyed and apparent conductivity (ECa) data were recorded at depths of 0.75 m and 1.50 m. ECa data were converted to electrical conductivity and salinity characterisation was conducted following U.S. Salinity Laboratory recommendations. Ordinary Kriging interpolation was used to map the spatial distribution and an iterative inversion model was used to map the vertical distribution of soil salinity. Model parameters were adjusted several times and the accuracy of different inversion algorithms was compared to obtain the best inversion effect. As a result, the Multilevel Orthogonal Inversion model was developed to characterise 3D soil salinity for different land-use types. Due to crop activities including irrigation, managed land use types (apple orchard and winter wheat plots) typically exhibited weaker salinity than natural systems (desert and vegetation plots) but greater spatial variability overall. The proposed framework combining EM (electromagnetic) sensing and the 3D inversion algorithm can effectively characterise and visualise soil salinity for the entire soil profile, which is important for land evaluation and improvement.

Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.

Spring crops in three year rotations reduce weed pressure in winter wheat

Downy brome (Bromus tectorum L.) is a problematic weed for the conventional fallow/winter wheat (F/W) production system in the low precipitation-region (<350 mm yr−1) of the Pacific Northwest. A 4-yr field experiment was conducted to determine if incorporating spring barley (B, Hordeum vulgare L.) or spring carinata (C, Brassica carinata A. Braun) into 3-yr crop rotations with W would benefit weed management. The experimental design was a split-plot with four replications where each phase was present every year for the following rotations: 1) F/W, 2) F/W/B, and 3) F/W/C. Reduced tillage, consisting of a single undercutting operation with a wide-blade sweep, and herbicides were used to control weeds during the fallow period. The seeded plots were subdivided in three different weed management areas: a weed-free area where weeds were pulled by hand, a weedy area with no weed control and a general area where weeds were chemically controlled. Weed density and cover per species and W yield were evaluated in each rotation. Grass cover and density after one and two complete cropping cycles were significantly higher in F/W than in F/W/B and F/W/C. Reduction in density and cover of total weeds was found after two cycles. However, differences in community biodiversity were only found between F/W, and F/W/B or F/W/C in 2017. Winter wheat plots of F/W had more downy brome than F/W/B or F/W/C indicating the greater capacity of the latter to control this weed. In 2018, the 3-yr rotation with barley had greater winter wheat grain yield compared with F/W when weeds were not present though weeds were more competitive in F/W/B. Intensifying the F/W cropping system into a 3-yr crop rotation of W followed by spring barley or spring carinata may reduce weed infestations of winter annual grasses that are difficult to control in W and the most competitive due to larger similarities in their life cycle with this crop.

Weed responses to fallow management in Pacific Northwest dryland cropping systems.

A two-year rotation of summer fallow (SF)/winter wheat (WW) is the most common cropping system in low precipitation areas of the U.S. Pacific Northwest (PNW). In SF, multiple tillage operations are used to manage weeds and maximize soil water storage and potential WW yield. Reduced tillage fallow (RTF) is an alternative to SF that leaves >30% of the previous crop’s residue on the surface. A four-year (2014–18) field study was conducted to evaluate the influence of SF and RTF on weed species density, cover and composition in dryland WW; determine if changes in these weed infestation attributes have any influence on crop density and yield; and evaluate economic costs of each type of fallow management. The experimental design was randomized complete block with four replications where each phase of SF/WW and RTF/WW rotations was present every year. Individual plots of WW were divided into a weedy sub-plot with no weed control, general area with chemical weed control, and weed-free sub-plot where weeds were manually removed. Infestations of annual grass and other weeds in weedy sub-plots increased throughout the study. Grass weed cover, consisting mainly of downy brome (Bromus tectorum L.), and total weed cover were significantly lower in WW following RTF than following SF in all years except 2018. Densities of grass and total weeds were similar in both fallow managements indicating that weed plants were larger in WW following SF than following RTF due to earlier or faster emergence. Grass cover differences were not found in general areas likely because of a reduced seedbank. When weeds were present, mean yield of WW was higher following RTF than SF indicating that weeds were less competitive in RTF. Reduced tillage fallow could improve weed management in fallow/WW cropping systems of the PNW compared to SF/WW, particularly if the most problematic species are grasses.

Aphids (Homoptera: Aphididae) on Winter Wheat: Predicting Maximum Abundance of Metopolophium dirhodum.

In Central Europe, the most abundant aphid infesting the leaves of small grain cereals is Metopolophium dirhodum (Walker) (Homoptera: Aphididae). Annual variation in its seasonal dynamics was evaluated using a 25-yr series of standardized weekly censuses of winter wheat plots. M. dirhodum made up >50 % of the aphids on the foliage. Date of immigration (8 May–3 July), length of period of population increase (0–9 wk), and date of attaining maximum abundance (28 May–22 July) varied greatly. For the prediction, we regressed maximum numbers/tiller on numbers recorded in the first week after heading. The regression of maximum abundance on nonzero aphid counts revealed a critical number of ≥1.50 aphids/tiller, which if exceeded resulted in a harmful maximum abundance of ≥10 aphids/tiller at the peak. Zero aphid counts resulted in 10% of cases with a harmful maximum abundance. Using this regression for prediction will result in 18% of the recorded cases being false negatives and 9% false positives. Parallel annual variation in the average maximum numbers of M. dirhodum, Sitobion avenae (Fabricius) (Homoptera: Aphididae), and Rhopalosiphum padi (Linné) (Homoptera: Aphididae) indicated the following factors that affected their abundance: temperature in winter and host plant quality. The predictions apply only in areas where M. dirhodum is holocyclic and aphids do not overwinter in wheat stands.