Intra-field Variations Research Articles

Factors of intra-field phytomass variability in steppe agricultural landscapes of Kazakhstan

The yield of grain crops and its predictability largely depends on the intra-field heterogeneity of landscape conditions. We used the example of steppe agricultural landscapes in the Akmola region to solve the following objectives: 1) to evaluate the informative value of NDVI for assessing yield depending on hydrothermal conditions; 2) to determine the effect of intra-field patchiness on NDVI values and yield; 3) to determine the dependence of intra-field variation of phytomass on the tract and facies structure and hydrothermal conditions. We used data on wheat and barley yields and intra-field NDVI variability in 80 field plots in 2009‒2013, data on the tract and facies structure, and hydrothermal parameters. We studied the relationships between spatial and temporal variability of phytomass, landscape and hydrothermal factors by means of correlation and variance analysis. It was established that NDVI was only partially informative for direct assessment of grain yield. The index values for July dates better reflect the yield than in June and August. The hypothesis that NDVI patchiness in the field area depends on the cultivated crop was not confirmed. The yield in fields with complex facies structure is usually lower than in homogeneous fields. In dry years, the intra-field variation of phytomass is less pronounced than in wet years. The proximity to the hill is a significant factor that increases the spatial variability of phytomass due to the additional introduction of moisture and sediment along the rills. High facies patchiness either reduces yield or increases its temporal variability and reduces predictability.

Crop resilience via inter-plant spacing brings to the fore the productive ideotype.

Natural selection favors the competitive ideotype, enabling native plants to survive in the face of intense competition. The productive ideotype is the goal of artificial selection to achieve high crop yields via the efficient use of resources in a self-competition regime. When breeding is established under inter-genotypic competition, the competitive ideotype dominates and may fictitiously become selectable. The productive ideotype becomes selectable at the nil-competition regime, where widely spaced individuals prevent plant-to-plant interference for any input. Principal reasons bring to the fore the productive ideotype that combines low competitiveness and improved plant yield efficiency. Crop spacing via the productive ideotype is mandated to alleviate the varying optimum density and ensure efficient use of resources inter-seasonally, cope with intra-field variation and optimize resource use, compensate for missing plants and promote stability, counteract unpredictable stresses and offer a buffer against environmental diversity, and adopt low-input agriculture to conserve natural resources and the environment. For breeding toward the productive ideotype, nil-competition is the due condition to overcome the confounding effects of competition, maximize phenotypic differentiation and facilitate selection from an early segregating generation, optimize heritability due to moderated environmental variance and experimental designs that sample spatial heterogeneity, apply high selection pressure focusing exclusively on the targeted genotype, and avoid the risk of bias selection or loss of desired genotypes due to proximity to empty hills. The view of a modern crop variety composed of genotype(s) belonging to the productive ideotype is a viable option to reach crop resilience serving sustainability in enormously fluctuating agroecosystems.

Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning.

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas. Both deep learning strategies showed improvements upon traditional statistical learning approaches including linear regression and gradient boosted decision trees. First, we explicitly accounted for variation across developmental stages using a 3D convolutional neural network (CNN) architecture that captures both spatial and temporal dimensions of UAV images from multiple time points throughout one growing season. 3D-CNNs achieved low prediction error on the test set, with a Root Mean Squared Error (RMSE) of 8.8% of the mean yield. For the second strategy, a 2D-CNN, we considered only spatial relationships among pixels for image features acquired during a single flyover. 2D-CNNs trained on images from a single day were most accurate when images were taken during booting stage or later, with RMSE ranging from 7.4 to 8.2% of the mean yield. A primary benefit of convolutional autoencoder-like models (based on analyses of prediction maps and feature importance) is the spatial denoising effect that corrects yield predictions for individual pixels based on the values of vegetation index and thermal features for nearby pixels. Our results highlight the promise of convolutional autoencoders for UAV-based yield prediction in rice.

PRECISION FARMING IN FLORICULTURE

Precision farming is a science base modern technology which provided management concept based on observation and response to intra-field variations. New technologies such as Global Positioning Systems (GPS), sensors, satellites or aerial images and Geographical Information Systems (GIS) are utilized to assess and analyse variations in agricultural and horticultural production. In this technology have two primary goals that are (i) optimum return (ii) preserving resource. Wireless Sensor Networks has crucial role to management of water resources, to assess the optimum point of harvesting, to estimate fertilizer requirements and to predict crop performance more accurately, disease and pest hazard also. Sensors use to precision farming technology in horticulture, which increasing productivity, decreasing production costs and minimizing the environmental impact of farming. Though precision farming has vital role in Agriculture and Horticulture sector but, no so popular due to high cost of technology and need high speed internet facility.

Origin and Biodegradation of Crude Oils from the Northernmost Fields in the Bolivar Coastal Complex (Zulia State, Venezuela)

The organic geochemical features of 30 sampled oils from the northern Bolivar Coastal Complex (Lake Maracaibo Basin, NW Venezuela) were examined by combining carbon isotope, classical biomarker, and extended diamondoid analyses to clarify source facies and to assess the thermal maturity and extent of biodegradation of the oils analyzed. In this work, oils are understood as a mixture of two episodes of petroleum generation from the La Luna Formation: a paleobiodegraded oil pulse during Paleogene times and a late pulse having a higher maturity in the post-Oligocene. For the oil samples analyzed, results revealed a mixing composed of different proportions of almost a terrestrially derived siliciclastic-sourced and a purely marine carbonate-sourced oil pulse. Moreover, two main groups of oils were identified by means of hierarchical cluster analysis. Finally, inter- and intrafield variations in the extent of biodegradation were also assessed using two classification schemes (Peters and Moldowan, and Manco scales).

Characterizing interlayer edge placement with SEM contours

Background: The continuous scaling of integrated circuit requires not only a very good control of the device critical dimensions but also a very accurate control of the device overlay between layers to achieve satisfactory yields. These two critical factors can be combined to a single metric called interlayer edge placement error (EPEinterlayer) that quantifies the process margin necessary to keep a safe separation, extension, or overlap between the edges of a pattern in one layer with respect to another pattern in a second layer. Aim: The purpose of this work is to characterize with scanning electron microscopy (SEM), the EPEinterlayer and overlay variances of complex contact shapes relative to a poly layer to assess the contributions of the systematic and random EPEinterlayer. Approach: SEM images of a few etched patterns were recorded sequentially for both contact and poly features at the same locations on the wafer. Then, SEM contours were extracted, aligned, and overlapped to derive EPEinterlayer. One experiment was focusing on intrawafer EPEinterlayer characterization whereas another was studying more specifically intrafield overlay variations. For the latter experiment, systematic overlay errors were added to facilitate the comparison of the SEM-based method with respect to a reference image-based overlay (IBO) method. Results: The earliest direct metrology of EPEinterlayer and overlay in device enabled by this work shows a very high variability of EPEinterlayer and overlay errors across wafer and across field. Conclusions: By directly measuring the EPEinterlayer on devices that are not accessible by the standard method (optical IBO on OL structures), we showed the feasibility of this metrology and observed more dimensional variance in devices than recognized with IBO, thereby enabling better control of device pattern variation.

Pb isotope compositions of galena in hydrothermal deposits obtained by drillings from active hydrothermal fields in the middle Okinawa Trough determined by LA-MC-ICP-MS

The Pb isotope compositions of galena in hydrothermal deposits obtained by drillings from two hydrothermal fields in the middle Okinawa Trough were studied. One of the study fields was the Iheya North field located on a volcanic complex and the other was the Izena field located in the sediment-filled caldera structure. LA-MC-ICP-MS was applied to directly measure Pb isotope compositions in individual galena grains which highlighted variations not only in regional scale, but also in microscopic scale so that changes of Pb isotope compositions within a galena grain can be tracked. Homogeneous Pb isotope compositions were found within the same hydrothermal site, irrespective of the mineral assemblage, texture and sampling depth beneath the seafloor. In contrast, the isotope compositions varied significantly between the two hydrothermal fields. The Pb isotope composition from the Iheya North field was isotopically close to the volcanic rocks of the Okinawa Trough, whereas that from the Izena field was more radiogenic with values intermediate between sediments and volcanic rocks of the Okinawa Trough. Within the Iheya North field, intra-field variation of Pb isotope compositions was recognized between two active sites 2.5 km apart from each other. The intra-field variation was recognized also in the Izena field. The sub-seafloor massive sulfide layer has a more sediment-like Pb isotope composition, compared to the inactive sulfide mound. These results illustrate that the Pb-mineralizing hydrothermal fluids originate from their local host rocks with/without sediment and that the isotope compositions of the galena grains reflect their metal sources; either the volcanic rocks and/or the sediments via water-rock interactions.

Linking canopy reflectance to crop structure and photosynthesis to capture and interpret spatiotemporal dimensions of per-field photosynthetic productivity

Abstract. Nitrogen and water availability alter canopy structure and physiology, and thus crop growth, yielding large impacts on ecosystem-regulating/production provisions. However, to date, explicitly quantifying such impacts remains challenging partially due to lack of adequate methodology to capture spatial dimensions of ecosystem changes associated with nitrogen and water effects. A data fitting, where close-range remote-sensing measurements of vegetation indices derived from a handheld instrument and an unmanned aerial vehicle (UAV) system are linked to in situ leaf and canopy photosynthetic traits, was applied to capture and interpret inter- and intra-field variations in gross primary productivity (GPP) in lowland rice grown under flooded conditions (paddy rice, PD) subject to three nitrogen application rates and under rainfed conditions (RF) in an East Asian monsoon region of South Korea. Spatial variations (SVs) in both GPP and light use efficiency (LUEcabs) early in the growing season were enlarged by nitrogen addition. The nutritional effects narrowed over time. A shift in planting culture from flooded to rainfed conditions strengthened SVs in GPP and LUEcabs. Intervention of prolonged drought late in the growing season dramatically intensified SVs that were supposed to seasonally decrease. Nevertheless, nitrogen addition effects on SV of LUEcabs at the early growth stage made PD fields exert greater SVs than RF fields. SVs of GPP across PD and RF rice fields were likely related to leaf area index (LAI) development less than to LUEcabs, while numerical analysis suggested that considering strength in LUEcabs and its spatial variation for the same crop type tends to be vital for better evaluation in landscape/regional patterns of ecosystem photosynthetic productivity at critical phenology stages.

Identification of High-Variation Fields based on Open Satellite Imagery

This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective. The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update of satellite imagery, hence coupling the geospatial data analysis to direct improvements for the farmers, contractors, and consultants.

Novel Barite Chimneys at the Loki's Castle Vent Field Shed Light on Key Factors Shaping Microbial Communities and Functions in Hydrothermal Systems.

In order to fully understand the cycling of elements in hydrothermal systems it is critical to understand intra-field variations in geochemical and microbiological processes in both focused, high-temperature and diffuse, low-temperature areas. To reveal important causes and effects of this variation, we performed an extensive chemical and microbiological characterization of a low-temperature venting area in the Loki's Castle Vent Field (LCVF). This area, located at the flank of the large sulfide mound, is characterized by numerous chimney-like barite (BaSO4) structures (≤ 1 m high) covered with white cotton-like microbial mats. Results from geochemical analyses, microscopy (FISH, SEM), 16S rRNA gene amplicon-sequencing and metatranscriptomics were compared to results from previous analyses of biofilms growing on black smoker chimneys at LCVF. Based on our results, we constructed a conceptual model involving the geochemistry and microbiology in the LCVF. The model suggests that CH4 and H2S are important electron donors for microorganisms in both high-temperature and low-temperature areas, whereas the utilization of H2 seems restricted to high-temperature areas. This further implies that sub-seafloor processes can affect energy-landscapes, elemental cycling, and the metabolic activity of primary producers on the seafloor. In the cotton-like microbial mats on top of the active barite chimneys, a unique network of single cells of Epsilonproteobacteria interconnected by threads of extracellular polymeric substances (EPS) was seen, differing significantly from the long filamentous Sulfurovum filaments observed in biofilms on the black smokers. This network also induced nucleation of barite crystals and is suggested to play an essential role in the formation of the microbial mats and the chimneys. Furthermore, it illustrates variations in how different genera of Epsilonproteobacteria colonize and position cells in different vent fluid mixing zones within a vent field. This may be related to niche-specific physical characteristics. Altogether, the model provides a reference for future studies and illustrates the importance of systematic comparative studies of spatially closely connected niches in order to fully understand the geomicrobiology of hydrothermal systems.

Image segmentation using mean shift for extracting croplands from high-resolution remote sensing imagery

A new image segmentation algorithm based on mean shift (MS) is proposed, with an objective to single out croplands in high-resolution remote sensing imagery (HRRSI). The algorithm is composed of two parts. First, in order to improve the discontinuity preserving smoothing of MS filtering for cropland HRRSI, normal and uniform kernels are used to filter inner fields and boundary areas, respectively. A new spectral bandwidth estimation is also developed for better suppressing intra-field variation. Second, a two-stage region-merging technique, with the second stage combining mutual best-fit rule and iterative thresholding, is implemented. An HRRSI scene is used for validation, the results of which indicate good performance of our method.

Characterisation of dissolved organic compounds in hydrothermal fluids by stir bar sorptive extraction - gas chomatography - mass spectrometry. Case study: the Rainbow field (36°N, Mid-Atlantic Ridge)

The analysis of the dissolved organic fraction of hydrothermal fluids has been considered a real challenge due to sampling difficulties, complexity of the matrix, numerous interferences and the assumed ppb concentration levels. The present study shows, in a qualitative approach, that Stir Bar Sorptive Extraction (SBSE) followed by Thermal Desorption – Gas Chromatography – Mass Spectrometry (TD-GC-MS) is suitable for extraction of small sample volumes and detection of a wide range of volatile and semivolatile organic compounds dissolved in hydrothermal fluids. In a case study, the technique was successfully applied to fluids from the Rainbow ultramafic-hosted hydrothermal field located at 36°14’N on the Mid-Atlantic Ridge (MAR). We show that n-alkanes, mono- and poly- aromatic hydrocarbons as well as fatty acids can be easily identified and their retention times determined. Our results demonstrate the excellent repeatability of the method as well as the possibility of storing stir bars for at least three years without significant changes in the composition of the recovered organic matter. A preliminary comparative investigation of the organic composition of the Rainbow fluids showed the great potential of the method to be used for assessing intrafield variations and carrying out time series studies. All together our results demonstrate that SBSE-TD-GC-MS analyses of hydrothermal fluids will make important contributions to the understanding of geochemical processes, geomicrobiological interactions and formation of mineral deposits.

Variability in microbial community and venting chemistry in a sediment-hosted backarc hydrothermal system: Impacts of subseafloor phase-separation

Phase-separation and -segregation (boiling/distillation of subseafloor hydrothermal fluids) represent the primary mechanisms causing intra-field variations in vent fluid compositions. To determine whether this geochemical process affects the formation of microbial communities, we examined the microbial communities at three different vent sites located within a few tens meters of one another. In addition to chimney structures, colonization devices capturing subseafloor communities entrained by the vent fluids were studied, using culture-dependent and -independent methods. Microbiological analyses demonstrated the occurrence of distinctive microbial communities in each of the hydrothermal niches. Within a chimney structure, there was a transition from a mixed community of mesophiles and thermophiles in the exterior parts to thermophiles in the interior. Beside the transition within a chimney structure, intra-field variations in microbial communities in vent fluids were apparent. Geochemical analysis demonstrated that different vent fluids have distinctive end-member compositions as a consequence of subseafloor phase-separation and -segregation, which were designated gas-depleted, normal and gas-enriched fluids. In comparison to gas-depleted and normal fluids, gas-enriched fluids harbored more abundant chemolithoautotrophs with gaseous component-dependent energy metabolism, such as hydrogenotrophic methanogenesis. Subseafloor phase-separation and -segregation may play a key role in supplying energy and carbon sources to vent-associated chemolithoautotrophs and subvent microbial communities.

Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models

The retrieval of biophysical variables using canopy reflectance models is hampered by the fact that the inverse problem is ill-posed. This leads to unstable and often inaccurate inversion results. In order to regularize the model inversion, a novel approach has been developed and tested on synthetic Landsat TM reflectance data. The method takes into account the neighbouring radiometric information of the pixel of interest, named object signature. The neighbourhood data can either be extracted from gliding windows, already segmented images, or using digitized field boundaries. The extracted radiometric data of the neighbourhood pixels are used to calculate 42 descriptive statistical properties that comprehensively characterize the spectral (co)variance of the image object (e.g. mean and standard deviation of the distributions, intercorrelations between spectral bands, etc.). Together with the habitual spectral signature of the pixel being inverted (6 variables), this object signature (42 variables) is used as input in an artificial neural net to estimate simultaneously three important biophysical variables (i.e. leaf area index, leaf chlorophyll, and leaf water content). The use of neural nets for the model inversion avoids time-consuming iterative optimizations and provides a computational effective way to consider simultaneously pixel and object signatures. In order to “learn” the relation between spectral signatures and biophysical variables, the neural nets were previously trained on large synthetic data sets. The data sets consist of pixel signatures and the corresponding signatures of image objects representing various agricultural fields. The signatures were simulated with the SAILH+PROSPECT canopy reflectance model, assuming largely varying intra- and interfield distributions of the model input parameters. To demonstrate the benefits of the object-based inversion, neural nets were also trained on the pixel signatures alone. For this purpose, the object signatures were simply replaced by randomly generated white noise—all other conditions being the same. The intercomparison based on 30,000 independent validation patterns showed that the proposed method significantly enhances the estimation accuracies: for example, the leaf area index (LAI) is estimated with a percental root mean square error (PRMSE) of 18.3% (object-based) compared to 25.1% (pixel based); the corresponding numbers for the leaf chlorophyll content are 12.6% compared to 15.9%; for the equivalent leaf water thickness, 10.6% and 13.9%, respectively. The benefit of the object signature was strongest for the LAI. Concerning this important biophysical variable, the novel concept accounted for almost one-half of the remaining unexplained variance of the traditional pixel-based approach. Increased accuracies were attributed to the fact that intrafield variations of biophysical canopy variables lead to object signatures that are modulated by the actual average leaf angle (ALA) of the canopy. Since ALA can be considered constant insight given an agricultural field, the concurrent use of pixel and object signatures significantly reduces confounding effects between LAI and ALA typical for traditional inversion approaches. Compared to competing approaches, the algorithm can also be applied to monotemporal imagery and does not require a priori information or the identification of crop type.

Intra-field yield variation over crops and years

Data and yield maps were obtained from farmers using their own combine harvester equipped with commercial systems for yield measurements and satellite positioning. Three quarters of the datasets collected were abandoned due to being incomplete or problems occurring when overlaying maps from different years. Intra-field yield variation was investigated in the remaining 82 fields. Some fields had data present for 4 years. The predominant crops were winter wheat and winter barley. The coefficient of variation (cv) ranged from 0.05 to 0.22, which was comparable to the residual cv usually found in agricultural experiments. In spring and winter rape the average cv was 0.15, cereals varied from 0.05 to 0.22. The distribution of yields within fields was generally non-normal; it was negatively skewed (left tailed) and broader than a normal distribution. Although the highest yielding crops and fields had the lowest intra-field variation, the distributions of yield were mostly negatively skewed. Regression analyses of intra-field yield pattern between years gave R 2 values from 0 to 0.81, with an average of 0.27. Similar crops and crops with small average yield differences gave the highest R 2 values, even when they were separated by more than one year in time. Prediction of intra-field variation, trend maps of temporal stability, and maps of classified management, could therefore be improved by developing them for individual crops based on the most appropriate previous crops. It appears unlikely that trend maps will account for much more than 1/3 of the annual intra-field variation on average. The possibilities for developing maps of classified management based on similar quality yield maps as obtained from farmers in the present investigation, appears limited because of the high frequency of erroneous data sets, systematic errors in the recorded data and their restricted yield predictive ability.

Effect of Reticle Erros on Systematic Intrafield Line Width Variations

We have studied the contributions of reticle line width ( CD) errors to systematic intrafield CD variations for 0.35 µ m i-line lithography by measuring electrical resistance of lines patterned in polysilicon films. Reticle errors were determined by independent measurements, and their effects on final printed dimensions were analyzed. Both reticles and steppers contribute to the systematic intrafield variation. In some cases, the total variance exceeds the sum of the variances individually associated with steppers and reticles. Because aberrations and reticle errors are not random, analysis of variance must take their correlation into consideration. We have observed cases in which this correlation and nonlinear imaging contribute significantly to total line width variance.

Prediction of large-scale communication in the Smørbukk fields from strontium fingerprinting

This paper discusses a new method which uses intra-field variability of formation water chemistry as a measure of degree of reservoir compartmentalization. If two reservoir units are in good flow communication, it is more likely that any water compositional variations that might have arisen would have homogenized by mixing processes (diffusion, dispersion, flow). On the other hand, a lack of flow communication is likely to inhibit water mixing and thus preserve variations in water compositions. Thus, whatever the cause of intra-field variations in water composition, two reservoir units that have a similar water chemistry are more likely to be in good flow communication than two units with different water compositions. In this study the isotopic composition of strontium ( 87 Sr/ 86 Sr) dissolved in the formation water was used to monitor formation water compositional variability. This natural isotopic tracer is particularly useful as it can be measured simply from conventional core samples by extracting residual salts which have precipitated in the pore spaces as a result of formation water evaporation during storage. The Smørbukk fields (Smørbukk North and Smørbukk South, Haltenbanken area, Norwegian Continental Shelf) comprise three Jurassic reservoir intervals, the Garn, Ile and Tilje formations. In Smørbukk North the main problems are vertical compartmentalization by areally extensive shales and lateral compartmentalization by extensive quartz cementation. In one well, residual salt analysis (RSA) shows that water 87 Sr/ 86 Sr compositions are extremely homogeneous in each of the Tilje 1 and 2 reservoir zones, indicating good internal vertical communication. However, the Tilje 1 and 2 waters are significantly different from each other, indicating that the shale that separates Tilje 1 and 2 is an effective barrier to vertical communication. A similar relation is seen in a second well, suggesting that the shale barrier is indeed laterally extensive. In the second well, Tilje 1 RSA data are the same as the results for that zone in the first well, indicating excellent lateral communication between the two wells. In Tilje 2, however, there is a distinct between-well difference in formation water composition, indicating that lateral communication is poor in this zone. In Smørbukk South, lateral connectivity is further complicated by faulting. Using RSA data from three wells it was possible to predict that: the Garn has good internal vertical communication, but is laterally divided into two isolated areas, probably the result of a sealing fault; the Ile forms three laterally isolated compartments; the Tilje is subdivided vertically into three flow units that are interpreted to have poor communication with each other in two of the wells. Laterally, Tilje 1 and 2 form two flow compartments, probably due to faulting.

An assessment of the need for routine use of organomercurial cereal seed treatment fungicides

Abstract Seed crops need protection from a build-up of seed-borne pathogens, but it is not known whether such protection is necessary for the large proportion of seed which is sown to produce a crop for consumption. Trials have been carried out on farms in Scotland over a 5-year period to assess the risk of sowing untreated cereal seed. Over 220 comparisons were made, with 41 stocks of spring barley and 36 of winter wheat. The mean establishment of winter wheat was increased from 59% to 65% with treatment, but that of barley was not affected. The amount of seedling disease in untreated crops was approximately 2–3 times the level in treated crops. There were, however, no significant differences of overall mean yield associated with the absence of treatment for either wheat or barley. There were significant differences in yield in individual comparisons, but these were unrelated to significant differences in establishment or seedling disease, and they were considered to be due to intrafield variation, and not to the effect of treatment. It is considered that seed treatment to protect against seed-borne pathogens, other than wheat or barley loose smut if known to be present, is not necessary for certified seed being sown to produce a non-seed crop. The use of organomercury in seed treatments in Great Britain could be reduced from 17 to 6 tonnes per annum.

A Uniformity Trial with Irrigated Broadcast Rice

This paper describes a uniformity trial with irrigated broadcast rice and shows the large inter- and intra-field variation which exists.The standard deviations decrease with the increased size of plots and vary from 20–1 per cent, with 1/436 acre plots to 7–8 per cent, with 1/22 acre plots.The decrease of the standard deviation is small with plots larger than 1/87 acre. This size of plot is considered to be the most practicable for field trials.The influence of proximity to bunds, irrigation channels and drains on increasing yields is discussed and methods of reducing errors due to this cause are suggested.It is shown that the use of a Latin square extending over more than one field has not produced a more accurate experiment than the use of randomised blocks. The latter method would therefore appear to be the more suitable for manurial and other trials, under the particular conditions of rice growing.