Modification and Properties of Cellulose Nonwoven Fabric—Multifunctional Mulching Material for Agricultural Applications

The paper describes a method of modifying a commercial viscose nonwoven fabric and its use as a modern mulching material in agriculture. The conducted research confirmed that the proposed modification of the viscose nonwoven fabric could be successfully used as a multipurpose and, above all, completely biodegradable nonwoven crop cover, which will eliminate the problem of disposal after the harvest period. Modified cellulose nonwoven fabric was obtained by staining with NB—BT helion brown, then padding with potassium nitrate (KNO3) solution (used as a fertilizer) and finally coating with polylactide (PLA) solution. The characterisation of the nonwoven fabric included structural analysis, physicochemical properties and mechanical tests. The modified cellulose nonwovens were used in the tunnel cultivation of tomatoes as a heat-retardant, water-absorbing, antiweed mulching material that prevents soil infestation and slowly releases fertilizers.

Assessment of Soil Erosion by RUSLE Model using Remote Sensing and GIS of Indla-Ghatkhed, District-Amravati, Maharashtra

The catchment boundary of Indla Ghatkhed watershed covers an area about 14..62 sq km. The erosion is a natural geomorphic process occurring continually over the earth’s surface and it largely depends on topography, vegetation, soil and climatic variables and, therefore, exhibits pronounced spatial variability due to catchments heterogeneity and climatic variation. This problem can be circumvented by discrediting the catchments into approximately homogeneous sub-areas using Geographic Information System (GIS). Soil erosion assessment modeling was carried out based on the Revised Universal Soil Loss Equation (RUSLE). A set of factors are involved in RUSLE equation are A = Average annual soil loss (mt/ha/year), R = Rainfall erosivity factor (mt/ha/year), k = Soil erodibility factor, LS = Slope length factor, C = Crop cover management factor, P = Supporting conservation practice factor. These factors extracted from different surface features by analysis and brought in to raster format. The output depicts the amount of sediment rate from a particular grid in spatial domain and the pixel value of the outlet grid indicates the sediment yield at the outlet of the watershed.

Prey removal in cotton crops next to woodland reveals periodic diurnal and nocturnal invertebrate predation gradients from the crop edge by birds and bats

Factors influencing the efficacy of insectivorous vertebrates in providing natural pest control services inside crops at increasing distances from the crop edge are poorly understood. We investigated the identity of vertebrate predators (birds and bats) and removal of sentinel prey (mealworms and beetles) from experimental feeding trays in cotton crops using prey removal trials, camera traps and observations. More prey was removed during the day than at night, but prey removal was variable at the crop edge and dependent on the month (reflecting crop growth and cover) and time of day. Overall, the predation of mealworms and beetles was 1-times and 13-times greater during the day than night, respectively, with predation on mealworms 3–5 times greater during the day than night at the crop edge compared to 95 m inside the crop. Camera traps identified many insectivorous birds and bats over crops near the feeding trays, but there was no evidence of bats or small passerines removing experimental prey. A predation gradient from the crop edge was evident, but only in some months. This corresponded to the foraging preferences of open-space generalist predators (magpies) in low crop cover versus the shrubby habitat preferred by small passerines, likely facilitating foraging away from the crop edge later in the season. Our results are in line with Optimal Foraging Theory and suggest that predators trade-off foraging behaviour with predation risk at different distances from the crop edge and levels of crop cover. Understanding the optimal farm configuration to support insectivorous bird and bat populations can assist farmers to make informed decisions regarding in-crop natural pest control and maximise the predation services provided by farm biodiversity.

Effect of Crop Cover and Stage of Crop Growth on Soil L-Glutaminase Activity

A pot culture experiment was conducted at glass house of Department of Soil Science and Agricultural Chemistry, College of Agriculture, Rajendranagar, Hyderabad. The aim of the present experiment was to study the influence of crop cover and stage of crop growth on soil L- glutaminase activity in an Alfisol and Vertisol. The experiment was under taken with six crops viz., two cereals (Rice, Maize), two legumes (Groundnut, Greengram), one oilseed (Sunflower) and one vegetable (Bhendi) crop. The experiment was conducted in Completely Randomized Block design with three replications along with the uncropped control. The results obtained with regard to the effect of these crops on soil L-glutaminase activity showed that there was an increase in enzyme activity with age of the crop upto 60 DAS and it varied with crops grown. The increased enzyme activity (μg of NH4+ released g-1 soil 4h-1) varied from 5.56 to 12.17 for groundnut, 5.58 to 11.25 for greengram, 5.43 to 10.87 for sunflower, 5.48 to 8.61 for rice, 5.39 to 8.23 maize and 5.31 to 7.92 for bhendi in Vertisol. In Alfisol the L-glutaminase activity (μg of NH4+ released g-1 soil 4h-1) under different crop cover found to vary from 6.72 to 13.59 (groundnut), 6.68 to 12.71 (greengram), 6.63 to 11.96 (sunflower), 6.61 to 10.25 (rice), 6.59 to 9.47 (maize), 6.62 to 9.26 (bhendi). A close perusal of the data indicates that the L-glutaminase activity followed the sequence groundnut > greengram > sunflower > rice > maize > bhendi, in both Alfisol and Vertisol.

N2O and CO2 Emissions from Bare Soil: Effect of Fertilizer Management

The paper presents the results of a laboratory experiment focused on the assessment of the effect of different methods of application of ammonium nitrate (TD—top dressing and DP—deep placement) on N2O and CO2 emissions from soil without crop cover. Nitrogen application increased soil N2O–N fluxes by 24.3–46.4%, compared to untreated soil (NIL). N2O–N emissions from TD treatment were higher by 12.7%, compared to DP treatment. Soil CO2–C fluxes from DP treatment were significantly higher by 17.2%, compared to those from NIL treatment. Nonetheless, the differences between soil CO2–C fluxes from DP and TD treatments, as well as from TD and NIL treatments, were of no statistical significance. The cumulative greenhouse gas (GHG) emissions (a sum of cumulative soil emissions of CO2–C and N2O–N after conversion to the equivalent of CO2–C) from both N-fertilized soils were similar, and higher by 20% than from untreated soil. The obtained data show that the effect of reduction of N2O–N soil emissions gained by deep placement of nitrogen fertilizer was completely lost through an increase in CO2–C emissions from the soil. This suggests that deep placement of nitrogen fertilizers in sandy soil without crop cover might not lead to a mitigation of soil GHG emissions.

Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform

Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based on remote sensing data using Google Earth Engine. We evaluated a machine learning algorithm—i.e., Random Forest to parameterize how the area in commodity varied in Indonesia. We used various predictors to estimate the productivity of various commodities based on multispectral satellite imageries (36 predictors) at 30-meters spatial resolution. The national commodity map has a relatively high accuracy, with an overall accuracy of about 95% and Kappa coefficient of about 0.90. The results suggest that the oil palm plantation was the highest commodity product that occupied the largest land of Indonesia. However, this study also showed that the land area in rubber, rice paddies, and cacao commodities was underestimated due to its lack of training samples. Improvement in training data collection for each commodity should be done to increase the accuracy of the commodity maps. The commodity data can be viewed online (website can be found in the end of conclusions). This data can further provide significant information related to the agricultural sectors to investigate food provisioning, particularly in Indonesia.

The ability of the green peach aphid (Myzus persicae) to penetrate mesh crop covers used to protect potato crops against tomato potato psyllid (Bactericera cockerelli)

In Central and North America, Australia and New Zealand, potato (Solanum tuberosum) crops are attacked by Bactericera cockerelli, the tomato potato psyllid (TPP). ‘Mesh crop covers’ which are used in Europe and Israel to protect crops from insect pests, have been used experimentally in New Zealand for TPP control. While the covers have been effective for TPP management, the green peach aphid (GPA, Myzus persicae) has been found in large numbers under the mesh crop covers. This study investigated the ability of the GPA to penetrate different mesh hole sizes. Experiments using four sizes (0.15 × 0.15, 0.15 × 0.35, 0.3 × 0.3 and 0.6 × 0.6 mm) were carried out under laboratory conditions to investigate: (i) which mesh hole size provided the most effective barrier to GPA; (ii) which morph of adult aphids (apterous or alate) and/or their progeny could breach the mesh crop cover; (iii) would leaves touching the underside of the cover, as opposed to having a gap between leaf and the mesh, increase the number of aphids breaching the mesh; and (iv) could adults feed on leaves touching the cover by putting only their heads and/or stylets through it? No adult aphids, either alate or apterous, penetrated the mesh crop cover; only nymphs did this, the majority being the progeny of alate adults. Nymphs of the smaller alatae aphids penetrated the three coarsest mesh sizes; nymphs of the larger apterae penetrated the two coarsest sizes, but no nymphs penetrated the smallest mesh size. There was no statistical difference in the number of aphids breaching the mesh crop cover when the leaflets touched its underside compared to when there was a gap between leaf and mesh crop cover. Adults did not feed through the mesh crop cover, though they may have been able to sense the potato leaflet using visual and/or olfactory cues and produce nymphs as a result. As these covers are highly effective for managing TPP on field potatoes, modifications of this protocol are required to make it effective against aphids as well as TPP.

Possibilities of using sentinel-2 satellite data to estimate crop cover and LAI of winter wheat at different stages of development

The study was conducted on a test plot located in the Yasnogorodsky district of the Tula Region; With a camera with fisheye lens photographs were taken at different points of the plot, each point with a geographical reference. Subsequently, the possible relationship between the information extracted from the classification of the photographs using the CAN-EYE software (LAI, percentage of vegetation) and the vegetation indices (Ratio, NDVI, SAVI and EVI) calculated with spectral values obtained from the different channels of Sentinel-2 (B2, B3, B4, B5, B6, B7, B8, B8a) were evaluated. Finally, best regression models obtained for each phase of the winter wheat development were used to create the maps of LAI and percentage of vegetation. According to our results, Sentinel-2 can be successfully used to map LAI in the studied region at the shooting stage of winter wheat development with accuracy of 85 %. At other stages and for percentage of vegetation the accuracy of the models was below 50 %.