Crop Residue Cover Research Articles

REMOTE SENSING FOR ESTIMATION OF INTENSITY AND EXTENT OF PLANT RESIDUE COVER

Abstract. Left-over crop residue on the surface is a measure of tillage intensity and soil management, and is an integral part of the conservation agriculture practice. Remote sensing can be successfully used to monitor the large area crop residue cover which is otherwise difficult through the conventional way, provided the spectrally similar crop residues and soil can be suitably differentiated. Hyperspectral reflectance (350–2500 nm) of various quantities of crop residue cover over red soil was measured with varying moisture contents in the residue. A broad spectrum near 2100 mm was identified for dry residue, which was not recorded in soil spectra. This could possibly be linked to the cellulose-lignin content. The cellulose absorption index (CAI) was evaluated for crop residue cover with moderate to good correlations, with strong dependency on the residue water content. A few narrow bands were identified to characterize both the cellulose-lignin (i.e. the CAI) and the water content, and could be incorporated in on-board multispectral sensors for regional estimation of crop residue over the soil surface.

A Sensitive Band to Optimize Winter Wheat Crop Residue Cover Estimation by Eliminating Moisture Effect

Crop residues can retain soil moisture and increase soil organic matter. Crop residue cover is also a hot issue in agricultural remote sensing. Crop residue cover can be estimated linearly with cellulose absorption index (CAI), while moisture of crop residues and soil would reduce the accuracy of crop residue cover estimation. Crop residue and soil were used as materials to carry out the laboratory experiment to reveal the impact of moisture on crop residue cover estimation and eliminate said impact. This paper discovered a sensitive band, R2005, which can invert water content of materials to eliminate moisture effect and improve estimation accuracy of crop residue cover. In terms of inverting water content, compared with two ratio water indices proposed in 2016 (R1.6/R1.5, R1.6/R2.0), using R2005 can increase R2 from 0.828 to 0.935 and decline root-mean-square error (RMSE) from 0.12 to 0.07. At the point of results validation, R2 is 0.958 and RMSE is 0.06, indicating R2005 has a high accuracy. Another advantage of R2005 is that it is more suitable to promote to actual production because of simple and efficient band calculation.

Estimation of Winter Wheat Residue Coverage Using Optical and SAR Remote Sensing Images

As an important part of the farmland ecosystem, crop residues provide a barrier against water erosion, and improve soil quality. Timely and accurate estimation of crop residue coverage (CRC) on a regional scale is essential for understanding the condition of ecosystems and the interactions with the surrounding environment. Satellite remote sensing is an effective way of regional CRC estimation. Both optical remote sensing and microwave remote sensing are common means of CRC estimation. However, CRC estimation based on optical imagery has the shortcomings of signal saturation in high coverage areas and susceptibility to weather conditions, while CRC estimation using microwave imagery is easily influenced by soil moisture and crop types. Synergistic use of optical and microwave remote sensing information may have the potential to improve estimation accuracy. Therefore, the objectives of this study were to: (i) Analyze the correlation between field measured CRC and satellite derived variables based on Sentinel-1 and Sentinel-2, (ii) investigate the relationship of CRC with new indices (OCRI-RPs) which combine optical crop residues indices (OCRIs) and radar parameters (RPs), and (iii) to estimate CRC in Yucheng County based on OCRI-RPs by optimal subset regression. The correlations between field measured CRC and satellite derived variables were evaluated by coefficient of determination (R2) and root mean square error (RMSE). The results showed that the normalized difference tillage index (NDTI) and radar indices 2 (RI2) had relatively higher correlations with field measured CRC in OCRIs and RPs (R2 = 0.570, RMSE = 6.560% and R2 = 0.430, RMSE = 7.052%, respectively). Combining OCRIs with RPs by multiplying each OCRI with each RP could significantly improve the ability of indices to estimate CRC, as NDTI × RI2 had the highest R2 value of 0.738 and lowest RMSE value of 5.140%. The optimal model for CRC estimation by optimal subset regression was constructed by NDI71 × σ V V 0 and NDTI × σ V H 0 , with a R2 value of 0.770 and a RMSE value of 4.846%, which had a great improvement when compared with the best results in OCRIs and RPs. The results demonstrated that the combination of optical remote sensing information and microwave remote sensing information could improve the accuracy of CRC estimation.

Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study

Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into environmental effects. Although remote-sensing-based techniques to estimate crop residue cover (CRC) have proven to be good tools for determining CRC, their application is limited by variations in the moisture of crop residue and soil. In this study, we propose a crop residue angle index (CRAI) to estimate the CRC for four distinct soils with varying soil moisture (SM) content and crop residue moisture (CRM). The current study uses laboratory-based tests ((i) a dry dataset (air-dried soils and crop residues, n = 392); (ii) a wet dataset (wet soils and crop residues, n = 822); (iii) a saturated dataset (saturated soils and crop residues, n = 402); and (iv) all datasets (n = 1616)), which allows us to analysis the soil and crop residue hyperspectral response to varying SM/CRM. The CRAI combines two features that reflect the moisture content in soil and crop residue. The first is the different reflectance of soil and crop residue as a function of moisture in the near-infrared band (833 nm) and short-wave near-infrared band (1670 nm), and the second is different reflectance of soils and crop residues to lignin, cellulose, and moisture in the bands at 2101, 2031, and 2201 nm. The effects of moisture and soil type on the proposed CRAI and selected traditional spectral indices ((i) hyperspectral cellulose absorption index; (ii) hyperspectral shortwave infrared normalized difference residue index; and (iii) selected broad-band spectral indices) were compared by using a laboratory-based dataset. The results show that the SM/CRM significantly affects the broad-band spectral indices and all other spectral indices investigated are less correlated with CRC when using all datasets than when using only the dry, wet, or saturated dataset. Laboratory study suggests that the CRAI is promising for estimating CRC with the four soils and with varying SM/CRM. However, because the CRAI was only validated by a laboratory-based dataset, additional field testing is thus required to verify the use of satellite hyperspectral remote-sensing images for different crops and ecological areas.

Do Cover Crops and Corn Residue Removal Affect Soil Thermal Properties?

Core Ideas Corn residue removal reduces soil thermal conductivity and volumetric heat capacity. Cover crops do not mitigate corn residue removal effects on thermal properties. Soil water content and soil bulk density are strong predictors of soil thermal properties. Soil thermal properties govern the transport and storage of heat in the soil. How management practices such as crop residue removal and cover crop (CC) use affect these soil properties is not well understood. For example, CCs could provide physical cover and improve soil properties after main crop residue removal and thus ameliorate the negative effects of residue removal on soil thermal properties. We measured changes in soil thermal properties including soil thermal conductivity, thermal diffusivity, volumetric heat capacity, and related properties under corn (Zea mays L.) residue removal with and without winter cereal rye (Secale cereale L.) under a 6‐yr irrigated no‐till continuous corn experiment on a silt loam in south central Nebraska. Cover crops did not affect thermal properties, but corn residue removal reduced field thermal conductivity by 12 to 41% and volumetric heat capacity by 6 to 49% during the growing season for the 0‐ to 5‐cm depth. Residue removal also reduced laboratory thermal conductivity by 19% at –0.03‐MPa and by 28% at –1.5‐MPa matric potential. Residue removal also reduced volumetric heat capacity in the laboratory by 23% at both matric potentials in the 0‐ to 10‐cm depth. Neither residue removal nor CC affected thermal diffusivity. Thermal conductivity was more strongly correlated with soil water content than with bulk density and soil organic C. Overall, CC had no effect on thermal properties, but corn residue removal could reduce the soil's ability to conduct heat relative to no removal.

Study on crop residue coverage in the state of Iowa and its importance in regional water supply

Transfer of nutrients and other toxic chemicals from agricultural fields to ground and surface water sources have become a major concern for regional planners in the State of Iowa. It depends on the strength of soil, residue coverage, terrain condition, and annual rainfall. South Fork portion of the Iowa River is fed by Tipton Creek, Honey Creek, Beaver Creek etc. watersheds and this area are located close to cities like Iowa Falls, Marshalltown, and Eldora. Land cover in this area is dominated by corn and soybean production. Presence of higher residue coverage means no-tillage farming practice in agricultural fields and it reduces the force with which raindrops fall on the soil. Correlation between residue coverage and Normalized Difference Tillage Index (NDTI) has been used as the method to identify the presence of tillage practice in agricultural fields. Residue coverage was less than 30% in 352 agricultural fields located adjacent to the South Fork portion of the Iowa River and the positive correlation between NDTI and residue coverage represents the effectiveness of data calculation procedure. A solution of these problems will require both policy and design based initiatives over a long period of time and implementation of existing natural resource management laws.

Mapping Crop Residue and Tillage Intensity Using WorldView-3 Satellite Shortwave Infrared Residue Indices

Crop residues serve many important functions in agricultural conservation including preserving soil moisture, building soil organic carbon, and preventing erosion. Percent crop residue cover on a field surface reflects the outcome of tillage intensity and crop management practices. Previous studies using proximal hyperspectral remote sensing have demonstrated accurate measurement of percent residue cover using residue indices that characterize cellulose and lignin absorption features found between 2100 nm and 2300 nm in the shortwave infrared (SWIR) region of the electromagnetic spectrum. The 2014 launch of the WorldView-3 (WV3) satellite has now provided a space-borne platform for the collection of narrow band SWIR reflectance imagery capable of measuring these cellulose and lignin absorption features. In this study, WorldView-3 SWIR imagery (14 May 2015) was acquired over farmland on the Eastern Shore of Chesapeake Bay (Maryland, USA), was converted to surface reflectance, and eight different SWIR reflectance indices were calculated. On-farm photographic sampling was used to measure percent residue cover at a total of 174 locations in 10 agricultural fields, ranging from plow-till to continuous no-till management, and these in situ measurements were used to develop percent residue cover prediction models from the SWIR indices using both polynomial and linear least squares regressions. Analysis was limited to agricultural fields with minimal green vegetation (Normalized Difference Vegetation Index < 0.3) due to expected interference of vegetation with the SWIR indices. In the resulting residue prediction models, spectrally narrow residue indices including the Shortwave Infrared Normalized Difference Residue Index (SINDRI) and the Lignin Cellulose Absorption Index (LCA) were determined to be more accurate than spectrally broad Landsat-compatible indices such as the Normalized Difference Tillage Index (NDTI), as determined by respective R2 values of 0.94, 0.92, and 0.84 and respective residual mean squared errors (RMSE) of 7.15, 8.40, and 12.00. Additionally, SINDRI and LCA were more resistant to interference from low levels of green vegetation. The model with the highest correlation (2nd order polynomial SINDRI, R2 = 0.94) was used to convert the SWIR imagery into a map of crop residue cover for non-vegetated agricultural fields throughout the imagery extent, describing the distribution of tillage intensity within the farm landscape. WorldView-3 satellite imagery provides spectrally narrow SWIR reflectance measurements that show utility for a robust mapping of crop residue cover.

Response of Soil Surface Greenhouse Gas Fluxes to Crop Residue Removal and Cover Crops under a Corn-Soybean Rotation.

Excessive crop residue returned to the soil hinders farm operations, but residue removal can affect soil quality. In contrast, cover cropping can return additional residue to the soil and improve soils and environmental quality compared with no cover cropping. Residue and cover crop impacts on soil surface greenhouses gas (GHG) emissions are undetermined and site specific. Thus, the present study was conducted to investigate the impacts of corn ( L.) residue management and cover cropping on GHG fluxes. The fluxes were measured from 2013 to 2015 using static chamber under corn and soybean [ (L.) Merr.] rotation initiated in 2000 at Brookings, SD. Treatments included two residue management levels (residue returned [RR] and residue not returned [RNR]) and two cover cropping (cover crops [CC] and no cover crops [NCC]). Results showed that RR under corn and soybean phases significantly reduced cumulative CO fluxes (2681.3 kg ha in corn and 2419.8 kg ha in soybeans) compared with RNR (3331.0 kg ha in corn and 2755.0 kg ha in soybeans) in 2013. The RR emitted significantly less cumulative NO fluxes than RNR from both the phases in 2013 and 2014, but not in 2015. The CC treatment had significantly lower cumulative NO fluxes than the NCC for corn and soybean phases in 2013 and 2014. We conclude that crop residue retention and cover cropping can mitigate the GHG emissions compared with residue removal and no cover cropping.

Crop rotation and residue management effects on soil enzyme activities, glomalin and aggregate stability under zero tillage in the Indo-Gangetic Plains

This 3-year study investigated the effects of crop rotation and residue management (surface retention of residues under zero tillage) on soil carbon pools, selected soil physical properties (soil aggregation, mean weight diameter) and biological properties (glomalin and ergosterol contents, soil respiration and enzyme activities) and their inter-relationships in a semi-arid soil of India under rainfed condition. The field study consisted of three different cropping systems (pearl millet-wheat, pearl millet-chickpea and pearl millet-mustard) in the main plots and three residue management practices (no residue, crop residue and Leucaena mulch cover) in the sub-plots, with three replications. All crops were cultivated adopting zero tillage (ZT). Surface (0–15 cm) samples were taken from fields after 3-years. Results revealed that crop rotation had no impacts on total soil organic C (SOC), Walkley-Black C (WBC) and soil aggregation, but significantly affected labile C. However, residue retention or addition significantly affected total SOC, labile C and soil aggregation. Plots under crop residue and Leucaena mulching had ∼30 and 26% higher large macroaggregates (>2 mm diameter) than no residue treated plots. A similar trend was observed for the proportion of macroaggregates, resulting in about 20 and 17% increase in mean weight diameter (MWD), respectively, in the crop residue and Leucaena added plots compared to the residue removal plots (∼0.91 mm). The residue retained plots had ∼40 and 13% higher soil glomalin content compared with residue removal (∼290 μg g−1 soil) and Leucaena added plots, respectively. Both soil glomalin and ergosterol contents were significantly (P < 0.05) related with large macroaggregates and there were significant relationships between glomalin and WBC concentration and glomalin and MWD in the surface soil. Glomalin, microbial biomass C, dehydrogenase, fluorescein diacetate (FDA), glucosidase and alkaline phosphatase activities were significantly increased by both crop rotation and residue management. Thus, the legume based cropping system (pearl millet-chickpea system) with ZT and residue retention under rainfed conditions had significant impact on soil aggregation and biological health, even after a short-term adoption.

Feasibility of satellite and drone images for monitoring soil residue cover

Monitoring crop residue cover (CRC) is one of the most important issues to achieve sustainable agriculture. Generally, it can be possible using remote sensing technology. New tools for remote sensing are regularly introduced by advancement of technology. In this study we used Landsat 8 OLI data plus images captured by a drone above the selected plots for remote sensing of CRC. For the remote sensing trails, a total of 23 indices were used to identify the CRC, of which 6 index; dead fuel index (DFI), Normalized difference tillage index (NDTI), Simple tillage index (STI), RATIO, Normalized difference index (NDI3), and NDI4 showed acceptable correlations with the CRC and DFI with R2 = 0.96 was the highest. Among the images captured by drone, those images taken from 10-meters height, showed acceptable correlation results up to 0.84, based on the three the RGB based indices; (R-G)/(G-B), 2×G-R-B and G/(R+G+B). Similar to satellite imaging, drone captured images can be used to estimate amount of CRC. This study showed that results from Landsat 8 OLI imagery is slightly more accurate than drone imagery for estimating CRC, however there are certain advantages associated with drone imagery including lower expenses, easy access and more control over data range desired plus more spatial and temporal resolutions.

Object-based satellite image analysis applied for crop residue estimating using Landsat OLI imagery

ABSTRACTThe main objective of this study is to apply an object-based image analysis (OBIA) approach to satellite image processing and determining crop residue cover (CRC) and tillage intensity. To achieve this goal, we collected ground truth data using line-transect method from 35 plots of farmlands with an area of 528 ha. Accordingly, Landsat Operational Land Imager (OLI) satellite image together with global positioning system (GPS)-based survey data set were considered for applying the OBIA methods and deriving CRC. To process the data, object-based image processing steps including segmentation and classification were applied to develop intelligent objects and establish classification using spectral and spatial characteristics of CRC. We developed three categories of rule sets including mean indices, tillage indices, and grey-level co-occurrence matrix (GLCM) texture features using the OBIA algorithms and assign class method. Results were validated against of ground control data set and were collected by GPS in field survey. Results of this study indicated that the brightness, normalised difference tillage index, and GLCM texture feature mean performed out as effective techniques. Overall accuracy and kappa coefficient (κ) were computed to be about 0.91 and 0.86; 0.93 and 0.90; 0.60 and 0.35, respectively, for the above-mentioned indices. The foregoing discussion has attempted to demonstrate that the remotely sensed data can be effective approach and substitute for ground methods, especially in large areas.

Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields

Quantifying the amount of crop residue left in the field after harvest is a key issue for sustainability. Conventional assessment approaches (e.g., line-transect) are labor intensive, time-consuming and costly. Many proximal remote sensing devices and systems have been developed for agricultural applications such as cover crop and residue mapping. For instance, current mobile devices (smartphones & tablets) are usually equipped with digital cameras and global positioning systems and use applications (apps) for in-field data collection and analysis. In this study, we assess the feasibility and strength of a mobile device app developed to estimate crop residue cover. The performance of this novel technique (from here on referred to as “app” method) was compared against two point counting approaches: an established digital photograph-grid method and a new automated residue counting script developed in MATLAB at the University of Guelph. Both photograph-grid and script methods were used to count residue under 100 grid points. Residue percent cover was estimated using the app, script and photograph-grid methods on 54 vertical digital photographs (images of the ground taken from above at a height of 1.5 m) collected from eighteen fields (9 corn and 9 soybean, 3 samples each) located in southern Ontario. Results showed that residue estimates from the app method were in good agreement with those obtained from both photograph–grid and script methods (R2 = 0.86 and 0.84, respectively). This study has found that the app underestimates the residue coverage by −6.3% and −10.8% when compared to the photograph-grid and script methods, respectively. With regards to residue type, soybean has a slightly lower bias than corn (i.e., −5.3% vs. −7.4%). For photos with residue <30%, the app derived residue measurements are within ±5% difference (bias) of both photograph-grid- and script-derived residue measurements. These methods could therefore be used to track the recommended minimum soil residue cover of 30%, implemented to reduce farmland topsoil and nutrient losses that impact water quality. Overall, the app method was found to be a good alternative to the point counting methods, which are more time-consuming.

Combined effects of cover crops, mulch, zero-tillage and resistant varieties on Striga asiatica (L.) Kuntze in rice-maize rotation systems

In low-input rice-maize rotation systems in the hills of central Madagascar, farmers deal with erratic rainfall, poor soils, high soil erosion risks and infestation by the parasitic weed Striga asiatica (L.) Kuntze. Practices combining zero-tillage with permanent soil cover by intercropped legumes and crop residue mulches — known as Conservation Agriculture (CA)— are proposed as remedy against soil and climatic constraints. Implications of these practices for S. asiatica are unknown. A 4-season factorial experiment compared the current farmer practice of rice − maize rotation, involving seasonal tillage and crop residue removal (CONV), with three rice − maize rotation systems following CA with different cover crops, i.e. Vigna unguiculata (cowpea) and Mucuna pruriens (CACM), Vigna umbellata (ricebean) (CARB), and Stylosanthes guianensis (CAST). Performance of two rice varieties, NERICA-4 and −9, with partial S. asiatica resistance, were compared with the locally popular B22. Parasite emergence time, numbers, and seed bank sizes were recorded.In all CA practices S. asiatica infection was significantly reduced. Best results were obtained with Stylosanthes guianensis (CAST). This species also suppressed ordinary weeds much better than other cover crops. With CAST, average parasite emergence was delayed by 7.5 days (in rice) and 6.3 days (in maize) and infection levels were reduced by 79% (in rice) and 92% (in maize) compared to the conventional farmer practice (CONV). NERICA varieties delayed S. asiatica emergence by 5.7 days (NERICA-9) and 9.7 days (NERICA-4) and reduced infection levels by 57% (NERICA-9) and 91% (NERICA-4) compared to B22. In maize the residual effect of resistance of NERICA-4 resulted in a delay of 7.5 days in S. asiatica emergence and a reduction of 60% in parasite numbers. The best combinations delay S. asiatica emergence by 17.8 days (CAST + NERICA-9) and 19.1 days (CARB + NERICA-4) and reduce the parasite infection levels by 96% (CAST + NERICA-9 or −4) to 98% (CARB + NERICA-4) in rice, compared to CONV + B22. After two full rice-maize rotation cycles S. asiatica seed numbers in the soil (0–10 cm) were 76% (CACM), 78% (CAST) and 86% (CARB) lower than under CONV. Even the combination of zero-tillage, crop residue mulching, cover crops and resistant rice varieties does not entirely prevent S. asiatica parasitism and seed bank increase. Additional measures, targeted to escaping weeds, would be required for fully effective and long-term control.

Surface Detention on Cropland, Rangeland, and Conservation Reserve Program Areas

Abstract. One of the factors contributing to overland flow on upland areas is water stored temporarily in a thin sheet on the soil surface as surface detention. This study was conducted to quantify surface detention on selected cropland, rangeland, and Conservation Reserve Program (CRP) sites. Surface detention was determined from the recession portion of runoff hydrographs corresponding with the period when rainfall had ceased but runoff continued. The hydrographs were generated from six previously reported rainfall simulation studies conducted on paired 3.7 m wide × 10.7 m long plots on which approximately 128 mm of rainfall was applied. Surface detention values were found to increase as crop residue or vegetative cover increased. Eleven fallow cropland sites in the eastern U.S. had surface detention values that varied from 1.7 to 4.6 mm. Surface detention on plots in southwestern Oklahoma containing Old World bluestem, no-till wheat, and conservation-till wheat was 9.4, 7.3, and 5.2 mm, respectively. No-till sorghum, tilled sorghum, no-till wheat, and tilled wheat plots in southeast Nebraska had surface detention values of 6.7, 4.5, 6.7, and 4.6 mm, respectively. Mean surface detention on no-till and tilled cropland sites in southwest Iowa containing corn residue was 7.2 and 5.9 mm, respectively. CRP study sites in southwestern Iowa had mean surface detention of 10.8 mm. When data from the six field studies were combined, mean surface detention values for fallow cropland, tilled cropland, no-till cropland, rangeland, and CRP areas were 3.1, 5.0, 6.9, 9.6, and 10.8 mm, respectively. Keywords: Depressional storage, Hydrographs, Hydrologic modeling, Overland flow, Runoff volume, Surface detention.

Improved crop residue cover estimates obtained by coupling spectral indices for residue and moisture

Remote sensing assessment of crop residue cover (fR) and tillage intensity can improve predictions of the environmental impact of agricultural practices and promote sustainable management. Spectral indices for estimating fR are sensitive to soil and crop residue water contents, therefore the uncertainty of fR estimates increases when moisture conditions vary. Our goals were to evaluate the robustness of spectral residue indices based on the shortwave infrared region (SWIR) for estimating fR and to mitigate the uncertainty caused by variable moisture conditions on fR estimates. Ten fields with center pivot irrigation systems (eight partially irrigated and two uniformly dry fields) were identified in Worldview-3 satellite imagery acquired for a study site in Maryland (USA). The fields were mid-irrigation at the time of imagery acquisition, allowing comparison of residue cover under dry and wet conditions. Fields were subdivided into approximately equal-size wedges within the dry and wet portions of each field, and the SWIR bands were extracted for each pixel. Two crop residue indices (Normalized Difference Tillage Index (NDTI); Shortwave Infrared Normalized Difference Residue Index (SINDRI) and a water index (WI) were calculated. Reflectance in each band was moisture-adjusted based on the WI difference between wet and dry wedges, and updated NDTI and SINDRI were calculated. Finally, the probability density distributions of fR estimated from the residue indices were calculated for each field. SINDRI was more robust than NDTI for estimating fR. Moisture corrections of spectral bands reduced the root mean square error of NDTI fR estimates from 22.7% to 4.7%, and SINDRI fR estimates from 6.0% to 2.2%. The mean and variance of the probability density distribution of fR estimated from residue indices, before and after moisture correction, were greatly reduced in the partially irrigated fields, but only slightly in fields with uniform water distribution. The estimation of fR should be based on SINDRI if appropriate bands are available, but fR can be reliably estimated by combining NDTI with a water content index to mitigate the uncertainty caused by variable moisture conditions.

Crop residue harvest impacts wind erodibility and simulated soil loss in the Central Great Plains

AbstractCrop residue removal can affect the susceptibility to soil wind erosion in climates such as those of the Central Great Plains, United States. Six on‐farm trials were conducted in Kansas from 2011 to 2013 to determine the effects of winter wheat (Triticum aestivum L.), corn (Zea mays L.), and grain sorghum (Sorghum bicolor (L.) Moench), residue removal at 0, 25, 50, 75, and 100% of initial height on soil wind erosion parameters. Those parameters include soil surface random roughness (RR), and wind erodible fraction (EF; aggregates &lt;0.84 mm), geometric mean diameter (GMD) and geometric standard deviation (GSD), stability of dry aggregates (DAS). Complete (100%) residue removal decreased the surface RR, increased EF, and decreased GMD. Overwinter EF values increased for five of six sites from fall 2011 to spring of 2012, particularly for the uppermost removal height (≥75%). Measured EF, GMD, GSD, DAS, and RR were also input into the Single‐event Wind Erosion Evaluation Program (SWEEP) to determine the effect of these parameters on simulated soil loss. The SWEEP simulated the wind velocity needed to initiate wind erosion as well as soil loss under each residue removal height at a wind velocity of 13 m s−1 for three hours. Threshold wind velocity required to initiate wind erosion generally decreased with increasing crop residue removal height, particularly for &gt;75% removal. Total estimated soil loss over the three‐hour event ranged from ≈2 to 25 Mg ha−1, depending on EF, GMD, GSD, RR, and percent crop residue cover. Removing 75% residue increased simulated wind erosion at three of six sites while removing 50% appears sustainable at all six study sites. Findings reinforce the need for site‐by‐site consideration of the potential amount of crop residue that may be harvested while mitigating wind erosion. Study results indicate the value of maintaining residue at &gt;75% of original height.

Agro-ecological functions of crop residues under conservation agriculture. A review

Conservation agriculture, which is based on minimum tillage, permanent soil cover and crop rotations, has widely been promoted as a practice to maintain or improve soil quality and enhance crop productivity. To a large extent, the beneficial effects of conservation agriculture are expected to be provided by permanent soil cover with crop residues. Surface crop residues play an important role for crop growth through their benefits on soil-related structural components and processes in the agro-ecosystem, referred to in this study as agro-ecological functions. Through a meta-analysis of the literature, we have studied the relative effects of surface crop residue levels on the performance of a set of agro-ecological functions compared with a no-till bare soil, i.e., without surface residues. The selected agro-ecological functions were soil water evaporation control, soil water infiltration, soil water runoff control, soil loss control, soil nutrient availability, soil organic carbon (SOC) stocks and gains, weed control and soil meso- and macrofauna abundance. The potential effects of crop residue cover were quantified using boundary line models. Our main findings were (1) 8 t ha−1 of residues were needed to decrease soil water evaporation by about 30% compared to no-till bare soil. (2) To achieve the maximum effect on soil water infiltration, water runoff and soil loss control, residue amounts of at least 2 t ha−1 were required. (3) The effect of increasing the amounts of surface crop residues on soil nutrient supply (N, P and K) was relatively low; the boundary line models were not significant. (4) The average annual SOC gain increased with increasing amounts of residues, with a mean of 0.38 t C ha−1 year−1 with 4 to 5 t ha−1 of residues. (5) Weed emergence and biomass can be reduced by 50% compared to a no-till bare soil with residue amounts of 1 t ha−1 or more. (6) There was a weak response in soil meso- and macrofauna abundance to increasing amounts of surface crop residues. The maximum effect corresponded to an increase of 45% compared to a no-till bare soil and was reached from 10 t ha−1 of residues. Our findings suggest that optimal amounts of surface residues in the practice of conservation agriculture will largely depend on the type of constraints to crop production which can be addressed with mulching.

Managing weeds in rice (Oryza sativa)-wheat (Triticum aestivum)-greengram (Vigna radiata) system under conservation agriculture in black cotton soils

Conservation agriculture (CA) does have several advantages over conventional tillage (CT) based agriculture in terms of soil health parameters. However, weeds are the major biotic constraint in CA, posing as a great challenge towards its adoption. Field experiment was conducted at Directorate of Weed Research , Jabalpur (MP) during 2012–14 to monitor weed dynamics, crop productivity, and soil health parameters in a rice (Oryza sativa L.)-wheat (Triticum aestivum L.)-greengram [Vigna radiate (L.) Wilczek] cropping sequence under CA system. The treatments consist of ZT (DSR)+Sesbania brown manuring – ZT (wheat) – ZT (greengram), with or without crop residue cover; CT (DSR)+Sesbania brown manuring – CT (wheat) – ZT (green gram), with or without crop residue cover; farmers practice, i.e. CT (TPR) – CT (wheat). The lowest total weed density was recorded under ZT (DSR)+Sesbania. Compared to CT, significantly lower density of Cyperus iria, but higher density of Caesulia axillaris was recorded under ZT; while CT (TPR) recorded the lowest population of P. minima and D. retroflexa during rice. Similarly, in wheat, significantly lower population of Phalaris minor and Chenopodium album was noticed under ZT. Highest grain yield (3.42 t/ha) was recorded with transplanted rice; whereas, higher yield of wheat was recorded when sown after DSR and crop residues were recycled. Tillage systems did not differ significantly in terms of soil respiration rate when crop residues were recycled. There was significant gain in soil organic carbon pool only when the complete conservation package of ZT along with crop residue recycling was adopted. A preliminary survey conducted in the adjoining localities of Jabalpur during 2012-13 showed lack of awareness among the farmers about the resource conservation technology (RCT) to grow wheat. Subsequently, the trials laid out in 3 farmers’ field during rabi seasons of 2012-13 and 2013-14 also showed that wheat performed much better under CA, as compared to the conventional farmers’ practice, resulting in lower cost of production, higher income and higher benefit: cost ratio. Green gram, sown with RCTs and chemical weed control measure in the same farmers’ fields following wheat, also provided higher B:C ratio and an additional net return of `28 975/ha over farmers practice. It was concluded that the RCTs like ZT and residue cover could be practiced in the black cotton soils to increase farmers’ income.

On the Ground or in the Air? A Methodological Experiment on Crop Residue Cover Measurement in Ethiopia

Maintaining permanent coverage of the soil using crop residues is an important and commonly recommended practice in conservation agriculture. Measuring this practice is an essential step in improving knowledge about the adoption and impact of conservation agriculture. Different data collection methods can be implemented to capture the field level crop residue coverage for a given plot, each with its own implication on survey budget, implementation speed and respondent and interviewer burden. In this paper, six alternative methods of crop residue coverage measurement are tested among the same sample of rural households in Ethiopia. The relative accuracy of these methods are compared against a benchmark, the line-transect method. The alternative methods compared against the benchmark include: (i) interviewee (respondent) estimation; (ii) enumerator estimation visiting the field; (iii) interviewee with visual-aid without visiting the field; (iv) enumerator with visual-aid visiting the field; (v) field picture collected with a drone and analyzed with image-processing methods and (vi) satellite picture of the field analyzed with remote sensing methods. Results of the methodological experiment show that survey-based methods tend to underestimate field residue cover. When quantitative data on cover are needed, the best estimates are provided by visual-aid protocols. For categorical analysis (i.e., >30% cover or not), visual-aid protocols and remote sensing methods perform equally well. Among survey-based methods, the strongest correlates of measurement errors are total farm size, field size, distance, and slope. Results deliver a ranking of measurement options that can inform survey practitioners and researchers.

Soil surface cover on irrigated rotations for potato ( Solanum tuberosum L.), dry bean ( Phaseolus vulgaris L.), sugar beet ( Beta vulgaris L.), and soft white spring wheat ( Triticum aestivum L.) in southern Alberta

Soil surface cover (crop residue and fall-seeded cover crops) during the nongrowing season is an important tenet of sustainable agriculture, reducing wind and water erosion risk and enhancing soil water conservation. Expansion of irrigated row crop production, namely, potato (Solanum tuberosum L.), dry bean (Phaseolus vulgaris L.), and sugar beet (Beta vulgaris L.), in southern Alberta in the late 1990s prompted a 12-year (2000 to 2011) study to evaluate rotations and soil management systems for improved soil quality. The study compared conservation (CONS) and conventional (CONV) management systems in three- to six-year rotations. Conservation management included cover crops; reduced tillage; feedlot manure compost addition; and solid-seeded, narrow-row dry bean production. Beginning in the third year of the study (2002), surface residue cover was estimated each spring prior to tillage or seeding. Averaged over 10 years (2002 to 2011), residue cover was significantly higher on CONS (15.6%) versus CONV (7.1%) management. Cover was significantly higher where wheat (Triticum aestivum L.) was the previous crop (33.7%) compared to narrow-row dry bean (9.7%), which was significantly higher than potato and sugar beet (6.5% to 6.7%), which were in turn significantly higher than wide-row bean (3.6%). Inclusion of a fall rye (Secale cereale L.) cover crop after potato or dry bean increased residue cover close to or more than 30%, the threshold required to reduce wind erosion risk. Long-term replacement of cereals with lower residue-producing crops (e.g., potato, dry bean, and sugar beet) on irrigated rotations in southern Alberta should be considered judiciously in light of potential negative effects on surface residue cover.