Uniformity of wheat yield and quality using sensor assisted application of nitrogen

Proximal sensing, or obtaining information from close range, is a potentially useful tool for measuring the crop nitrogen status in real-time The objective of this study was to use proximal sensing of crop canopy spectral reflectance to evaluate variable-rate application of nitrogen in terms of its effect on yield and grain quality of winter wheat (Triticum aestivum L.). The sensor used was the Hydro-Precise N-Sensor System. Yield and grain quality maps were used as a basis for full-scale field trials with winter wheat growing under four nitrogen application treatments: a large (274 kg ha^?1), recommended (167 kg ha^?1) and two sensor-assisted (167 kg ha^?1) rates. The recommended rate of 167 kg N ha^?1 was given in a three-split application that meets the present Danish regulations to reduce nitrogen leaching. These require arable farmers to decrease nitrogen fertilizer application to 90% of the economically optimal level. Each farm’s baseline is calculated to take into account land quality, land allocated to each crop, and crop rotation. In the two sensor-assisted applications the Hydro-Precise N-Sensor System directs the last two of the three-split N application. Grain samples were collected directly from the grain flow of a combine harvester and analysed for protein, water and starch content. Grain data were related to and compared with combine yield meter registrations. Within the field, the variances of protein yield (698–1208 kg ha^?1) and grain protein (9.5–13.4%) were large. The nitrogen application treatments affected the average protein content (10.5–12.3%) and grain yield (9.87–10.42 t ha^?1) strongly. The grain starch content was largest in the uniform and sensor applied systems and smallest in the high nitrogen application treatment. Applying nitrogen according to the Hydro-Precise N-Sensor System did not increase grain yield or the protein and starch contents. Minor differences only were observed in both protein content and yield between uniform-rate N application and sensor-based variable-rate N application.     

Optimization of corn plant population according to management zones in Southern Brazil

Precision agriculture relies on site-specific interventions determined by the spatial variability of factors driving plant growth. The main objective of this study was to assess the efficiency of variable-rate seeding of corn (Zea mays L.) with delineated management zones. This study involved two experiments carried out in Não-Me-Toque, Rio Grande do Sul, Brazil. For the first experiment, carried out in 2009/2010, management zones were delineated by the farmer’s knowledge of the crop field. The field was split into low (LZ), medium (MZ) and high (HZ) crop performance zones. In the second experiment, carried out in 2010/2011, management zones were delineated by overlaying standardized yield data from nine crop seasons (seven of soybean and two of corn). The experiment was carried out with a randomized block design with three management zones and five corn seeding rates ranging from 50 000 to 90 000 seeds per ha^?1. The soil was a Rhodic Hapludox with a subtropical climate. Optimization of the corn plant population within the field increased grain yield compared to the reference plant population (70 000 plants ha^?1). Yield increases in the LZ, due to corn plant population reduction in relation to the target population, were 1.20 and 1.90 Mg ha^?1 for first and second experiments, respectively. This resulted in economic gains of 19.8 and 28.7 %, respectively. Yield increases in the HZ were 0.89 and 0.94 Mg ha^?1, respectively, and were due to an increase in plant population in relation to the target population. This resulted in economic gains of 5.6 and 6.6 % for the first and second experiments, respectively. In the MZ, the adjustment of the target plant population was not necessary. Optimizing corn population according to management zones is a promising tool for precision agriculture in Southern Brazil.     

Underlying causes of yield spatial variability and potential for precision management in rice systems

Our current understanding of the mechanisms driving spatiotemporal yield variability in rice systems is insufficient for effective management at the sub-field scale. The overall objective of this study was to evaluate the potential of precision management for rice production. The spatiotemporal properties of multiyear yield monitor data from four rice fields, representing varying soil types and locations within the primary rice growing region in California, were quantified and characterized. The role of water management, land-leveling, and the spatial distribution of soil properties in driving yield heterogeneity was explored. Mean yield and coefficient of variation at the sampling points within each field ranged from 9.2 to 12.1 Mg ha^?1 and from 7.1 to 14.5 %, respectively. Using a k-means clustering and randomization method, temporally stable yield patterns were identified in three of the four fields. Redistribution of dissolved organic carbon, nitrogen, potassium and salts by lateral flood water movement was observed across all fields, but was only related to yield variability via exacerbating areas with high soil salinity. The effects of cold water temperature and land-leveling on yield variability were not observed. Soil electrical conductivity and/or plant available phosphorus were identified as the underlying causes of the within-field yield patterns using classification and regression trees. Our results demonstrate that while the high temporal yield variability in some rice fields does not permit precision management, in other fields exhibiting stable yield patterns with identifiable causes, precision management and modified water management may improve the profitability and resource-use efficiency of rice production systems.     

Evaluation of mid-season sensor based nitrogen fertilizer recommendations for winter wheat using different estimates of yield potential

Optical sensors, coupled with mathematical algorithms, have proven effective at determining more accurate mid-season nitrogen (N) fertilizer recommendations in winter wheat. One parameter required in making these recommendations is in-season grain yield potential at the time of sensing. Four algorithms, with different methods for determining grain yield potential, were evaluated for effectiveness to predict final grain yield and the agronomic optimum N rate (AONR) at 34 site-years. The current N fertilizer optimization algorithm (CNFOA) outperformed the other three algorithms at predicting yield potential with no added N and yield potential with added N (R² = 0.46 and 0.25, respectively). However, no differences were observed in the amount of variability accounted for among all four algorithms in regards to predicting the AONR. Differences were observed in that the CNFOA and proposed N fertilizer optimization algorithm (PNFOA), under predicted the AONR at approximately 75 % of the site-years; whereas, the generalized algorithm (GA) and modified generalized algorithm (MGA) recommended N rates under the AONR at about 50 % of the site-years. The PNFOA was able to determine N rate recommendations within 20 kg N ha^?1 of the AONR for half of the site-years; whereas, the other three algorithms were only able recommend within 20 kg N ha^?1 of the AONR for about 40 % of the site-years. Lastly, all four algorithms reported more accurate N rate recommendations compared to non-sensor based methodologies and can more precisely account for the year to year variability in grain yields due to environment.     

Sensing soil and foliar phosphorus fluorescence in <Emphasis Type="Italic">Zea mays</Emphasis> in response to large phosphorus additions

Additions of large loads of phosphorus (P) enriched animal manure to soils and the persistence of their environmental impact have been associated with continued water quality impairment in regions of high density of confined animal feeding operations. Foliar P in corn (Zea mays L.) and changes in labile P in Aquic Hapludults were determined following P application of 0–560 kg P ha^?1 as KH₂PO₄ and an application of Fe³⁺ (150 mg Fe³⁺ kg^?1) in field mini-lysimeters to develop calibrations of soil and plant nutritional responses. X-ray fluorescence (XRF) scanning of uppermost leaves of plants at the V2, V5, and V8 stages showed that foliar P proportionally increased with addition rates. Exchangeable and enzyme-labile P forms were effective indicators of foliar XRFS-P for up to 30 days after emergence. Phosphorus calibration curves developed for flag leaves showed that spatial distribution of foliar P (3.6, 4.2, and 5.3 g kg^?1) corresponded to field zones treated with 0, 15, and 30 kg P ha^?1 as dairy manure P for the past 18 years. Up-to-date crop uptake and availability of P in these Hapludults were best described by a square root function of soil XRFS-P and total exchangeable inorganic P (r² = 0.4; RMSE = 419 and 422 g ha^?1, respectively). Therefore, a timely knowledge of canopy P status and its linkage to actual soil P status supports in situ element-specific sensing and precision nutrient management in order to manage the declining use-efficiency in crops and reduce potential loss to the environment.     

Evaluation of agronomic and economic benefits of using RTK-GPS-based auto-steer guidance systems for peanut digging operations

Increasing the peanut (Arachis hypogea L.) digger efficiency by accurate placement over the target rows could minimize damaged pods and yield losses. Producers have traditionally relied solely on tractor operator skills to harvest peanuts. However, as peanut production has shifted to new growing regions in the Southeast US, producers face difficulties digging peanuts under conventional and new management schemes. The present study aimed to: (i) determine the effect of row deviations (RD) of the digger from the target row on peanut yield and quality, and (ii) determine the economic value of using RTK auto-steer guidance systems to avoid tractor deviations during peanut harvest. The study consisted of a randomized complete block design of tillage [conventional (CT) and strip tillage (ST)], row patterns [single (SR) and twin (TWR)] and row deviation (RD_0 mm, RD_90 mm, and RD_180 mm). The RD_90 mm and RD_180 mm treatments exemplify manual driving deviations compared to using an RTK auto-steer guidance system (RD_0 mm). Higher yields and higher net returns resulted from using the RTK auto-steer guidance system. Data showed that for every 20 mm row deviation, an average of 186 kg ha^?1 yield loss can be expected. Overall, yield was higher for the conventional tillage and twin row pattern treatments compared to the other treatments. Yield losses for the SR-CT treatment were higher as the row deviation increased compared with the TWR-CT treatment. In contrast, higher yield losses for TWR-ST compared to SR-ST were observed when deviations of 180 mm occurred instead of digging using the RTK auto-steer guidance system. While a farmer using an RTK auto-steer guidance system with an accuracy within 25 mm (RD_0 mm treatment) could potentially expect additional net returns of between 94 and 404 $ ha^?1 compared to those from row deviations of 90 mm, higher net returns of between 323 and 695 $ ha^?1 could be perceived if the guidance system is used instead of having row deviations of 180 mm. Therefore, the use of RTK auto-steer guidance system will allow growers to capitalize on the increases in yield potential by implementing changes in tillage and row patterns as those evaluated in this study.     

Probability of profitable yield response to nitrification inhibitor used with liquid swine manure on corn

Nitrification inhibitors (NI) can be used with liquid swine manure (LSM) to decrease potential NO₃ losses, but knowledge specifying when and where NI can increase corn (Zea mays L.) yields is limited. Eleven on-farm evaluation trials (OET) were conducted in 2009 and 15 in 2010 to identify site-specific factors for using Instinct (an encapsulated form of nitrapyrin) with LSM in Iowa. Farmers injected LSM in the fall in at least three field-long strips with and without NI. Yield responses (YR) to NI were calculated by dividing yield monitor data into 50-m cells within each field. Hierarchical models were used to estimate predictive probabilities of profitable YR for two categories of monthly average rainfall and soil drainage. On average, NI produced no YR in relatively normal 2009 and a 0.15 Mg ha^?1 YR in extremely wet 2010. The NI did not change late-season corn N status but half of corn stalk nitrate test (CSNT) samples were N deficient in 2009 and about 65 % in 2010. Fields receiving >90 cm March through August rainfall in 2010 were predicted 65 % more likely to have economic YR (>0.13 Mg ha^?1) than fields receiving <90 cm rainfall. Within-field variability in YR was about four times greater than among-field variability, but within field-level factors had no significant effects on YR. The NI effects may not have lasted long enough to increase yields across all OET and predictive probabilities suggest that NI may produce profitable YR only when spring and summer rainfall exceed the long-term averages by more than 40 %.     

Variable-rate nitrogen fertilization of winter wheat under high spatial resolution

Variable-rate application (VRA) addresses in-field variation in soil nitrogen (N) availability and crop response, and as such is a tool for more effective site-specific management. This study assessed the performance of a VRA system for on-the-go delivery of granular fertilizer in 7-m wide and 200-m long strips of a 2.4-ha wheat field. A randomized complete block design consisted of three treatment strips (a preplant uniform application of 100 kg N/ha, a preplant + in-season uniform farmer rate of 212 kg N/ha and a preplant + in-season VRA) within four blocks. The VRA prototype consisted of Crop Circle ACS-430 active canopy sensors, a GeoScout X data logger that processed the geospatial data to convey a real-time N rate signal (1 Hz) to a Gandy Orbit Air 66FSC spreader through a Raven SCS 660 controller. Crop monitoring included analysis of in-season soil and plant samples, water balance and grain yield. VRA delivered an economic optimum N rate using 72% less in-season N or 38% less total N (131 kg N/ha) than that applied by the farmer (212 kg N/ha). The reduction of total N inputs came about without any yield losses and translated to 58% N-use efficiency in comparison to 44% of the farmer practice and 52% of the preplant control. VRA also provided a much higher revenue over fertilizer costs, €68/ha and €118/ha higher than the preplant control and the farmer practice, respectively. The return of VRA per unit of N was equal to that of the large preplant application due to low leaching losses. Overall, the high-resolution VRA was superior in terms of environmental benefits and profitability with the least uncertainty to the farmer.     

Assessment and visualization of spatial interpolation of soil pH values in farmland

Site-specific farming entails fine-scale detection of field parameters that affect yield coupled with directing appropriate management inputs to select areas that improve field-scale cropping system profitability. Currently, limited technologies are available to evaluate spatial variability in soil properties on a fine scale (submeter resolution). Therefore, information is typically generated by collecting discrete samples and utilizing spatial interpolation to estimate data for the unsampled locations. In this study, soil pH samples were collected from a 12.15 ha agricultural field in northwest Missouri using two grid-sampling regimes: 0.11 ha with 110 samples and 0.98 ha with 12 samples. Three spatial interpolation methods (inverse distance weighted, spline and kriging) were tested to evaluate the effects of interpolation on unsampled locations. In addition to quantitative validation evaluations, results were also assessed by 2D visualization and 3D visualization. Although each assessment approach provided useful information, the inverse distance weighted technique overall better-estimated soil pH values as determined by a combination of all three approaches.     

Long-term impact of a precision agriculture system on grain crop production

Research is lacking on the long-term impacts of field-scale precision agriculture practices on grain production. Following more than a decade (1993–2003) of yield and soil mapping and water quality assessment, a multi-faceted, ‘precision agriculture system’ (PAS) was implemented from 2004 to 2014 on a 36-ha field in central Missouri. The PAS targeted management practices that address crop production and environmental issues. It included no-till, cover crops, growing winter wheat (Triticum aestivum L.) instead of corn (Zea mays L.) for field areas where corn was not profitable, site-specific N for wheat and corn using canopy reflectance sensing, variable-rate P, K and lime using intensively grid-sampled data, and targeting of herbicides based on weed pressure. The PAS assessment was accomplished by comparing it to the previous decade of conventional, whole-field corn-soybean (Glycine max L.) mulch-tillage management. In the northern part of the field and compared to pre-PAS corn, relative grain yield of wheat in PAS was greatly improved and temporal yield variation was reduced on shallow topsoil, but relative grain yield was reduced on deep soil in the drainage channel. In the southern part of the field where corn remained in production, PAS did not lead to increased yield, but temporal yield variation was reduced. Across the whole field, soybean yield and temporal yield variation were only marginally influenced by PAS. Spatial yield variation of all three crops was not altered by PAS. Therefore, the greatest production advantage of a decade of precision agriculture was reduced temporal yield variation, which leads to greater yield stability and resilience to changing climate.     

Analysis of soil water availability by integrating spatial and temporal sensor-based data

An efficient irrigation system should meet crop demands for water. A limited water supply may result in reductions in yield, while excess irrigation is a waste of resources. To investigate water availability throughout the growing season, on-the-go sensing technologies (field elevation and apparent electrical conductivity) were used to analyze the spatial variability of soil relevant to its water-holding capacity. High-density data layers were used to identify strategic sites to monitor changes in plant-available water over time. To illustrate this approach, nine locations in a 37-ha agricultural field were selected for monitoring the soil matric potential and temperature at four depths (18, 48, 79 and 109 cm) using wireless technology. Using a linear regression approach, a field-specific model was developed that quantified plant-available water at every field location and at specific points in time. Further analysis was used to quantify the percentage of the field that undergoes a potential shortage in water supply. These results could be used to optimize irrigation scheduling and to assess the potential for variable-rate irrigation.     

Delineation of specific management areas for coffee cultivation based on the soil–relief relationship and numerical classification

Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31.7 ha from which samples were collected at depths of 0.00–0.20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30.4l ha^?1) and best coffee beverage quality (61 sacks ha^?1). Based on the results, we believe that multivariate analysis, geostatistics and the soil–relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production.     

Economic analysis for smart sprayer application in wild blueberry fields

The wild blueberry industry is spending over $80 million CAD per year on agrochemicals for 93 000 ha under production in North America. A pressing need to reduce agro-chemical usage and production cost has resulted in the development of a smart sprayer for spot-application of agrochemicals in wild blueberry fields. This paper encompasses the economic analysis to determine the potential savings for spot-applications of agrochemicals using a smart sprayer. The economic analysis compared the smart sprayer with two other commercially available sprayers (basic and swath control). The swath control sprayer and smart sprayer both featured GPS auto-steer and boom section control to reduce over-spray in already applied areas based on GPS position. The basic sprayer used a foam marker for guidance with no swath control management. The smart sprayer featured a machine vision system that automatically detected target areas in the field further reducing agrochemical input by shutting individual nozzles off in non-target areas in the field. The cost analysis was performed to compare the different features of the sprayer technologies, i.e., base sprayer, additional technology, training, usage, repair and maintenance. The additional components installed on the smart sprayer were justified in terms of agrochemicals/water savings via spot-applications, tractor fuel and operator’s time. The application total cost was $2052 ha^?1 using the basic sprayer, $1799 ha^?1 using the swath control sprayer, and $1138 ha^?1 using the smart sprayer over a 2 year production cycle of the selected fields that were used in this study. The payback period ranged from 2.0 years (60 ha field size) to 9.8 years (20 ha field size) using the swath control sprayer. The payback period ranged from 11 months (60 ha field size) to 3.5 years (20 ha field size) when using the smart sprayer. Results revealed that the smart sprayer had significant advantage from both an environmental and economic perspective over the other two sprayers.     

Improving grass silage production with controlled traffic farming (CTF): agronomics,system design and economics

Grassland silage management is generally semi-organised with no conscious attempt to re-use wheel ways as with arable fields. The total number of machine passes can be 15 or more with normal traffic (NT) systems resulting in potentially large areas of a field suffering from direct damage to the crop and soil. Literature suggests there can be grass dry matter yield reductions of 5 to 74% under NT through compaction and sward damage, with a mean of 13% in the UK. Commercially available grass forage equipment with widths of 3 to 12 m set up for controlled traffic farming (CTF) could reduce trafficked areas (which is typically 90% to 80% for NT) to 40% to 13% for CTF. This study compared grass dry matter yield between CTF and NT for a three-cut silage system based on a 9 m working width in a permanent silage field in the southwest of Scotland, UK in 2015. Results showed a 13.5% (0.80 t ha^?1) increase in yield for CTF for the 2nd and 3rd cuts combined. The CTF trafficked area covered was 57% less than the NT system (30.4% compared to 87.4%) over the three silage cuts. An economic analysis based on a 13% increase in dry matter yield (for 2- and 3-cut systems) and a reduction in trafficked area from 80% (for NT) to between 45% and 15% (for CTF), increased the yield by between 0.53 t ha^?1 and 1.36 t ha^?1 for 2- and 3-cut systems, respectively with an equivalent grass value of between £38 ha^?1 and £98 ha^?1. Introducing CTF for a multi-cut grass silage system is cost-effective by increasing yields due to a reduction in compaction and sward damage.

     

Is data needed from every field to determine in-season precision nitrogen recommendations in winter wheat?

The research reported here seeks to determine whether it is necessary to obtain optical reflectance measurements with a GreenSeeker^® handheld sensor from each field to make accurate in-season nitrogen application recommendations for winter wheat, and how much precision—and profit—would be lost by moving from site-specific (or field-specific) optical reflectance sampling to region-level sampling. The approach used was to estimate a separate linear response-plateau regression every year using yield and optical reflectance data from randomized complete block experiments. Profits from region-level sampling and field-level sampling were statistically indistinguishable, but this result was mostly due to both being imprecise. Furthermore, the region- and field-based sampling systems were no better than break-even with the historical extension advice to apply preplant anhydrous ammonia at 90 kg ha^?1. The approach of estimating a new regression every year is too imprecise, whether at the field or region level. This research goes beyond past research by accounting for the uncertainty in the estimated relationships. The poor performance of the systems is directly related to the imprecise relationship between yield and optical reflectance responses to nitrogen.     

Spectral indices from aerial images and their relationship with properties of a corn crop

Identification of areas with similar restrictions to crop productivity could improve the efficiency to manage agricultural systems, guarantee stable yields, and reduce the effect of droughts in rainfed systems. The ability of any vegetation index to discriminate N and moisture-related changes in leaf reflectance would present an important advantage over the present diagnostic system which involves soil-testing for moisture and available N. The purpose of the study was to calibrate different vegetation indices regarding their capacity to identify water and nitrogen availability for rainfed corn crops in the semiarid Pampas of Argentina. A field experiment with corn with a control without fertilization (N0), and fertilized with 120 kg ha^?1 of nitrogen (N120) was used. Two sites, Low (L) and High (H), were identified within the field, according to their altimetry, a multi-spectral aerial photography was taken from a manned airplane during flowering stage of the corn crop, and four spectral indices were calculated (NDVI, green NDVI, NGRDI, (NIR/GREEN)-1). At six georeferenced points at each site soil texture, organic matter, available phosphorus, nitrogen and moisture contents as well as corn aerial biomass and grain yield were determined. The two sites differed in most of the evaluated soil properties, crop biomass and grain yield. The spectral information obtained at crop flowering showed clear differences between sites H and L for all four indices, indicating that any of these would be able to detect the differences in soil moisture and fertility among these environments. Both (NIR/GREEN)-1 and green NDVI had the best correlation with crop yield determined in the field, and therefore could be considered most appropriate for estimating corn yields from images taken at flowering. For estimation of N requirements, green NDVI differentiated best between fertilized and non-fertilized crop in the moisture limited environment (H), while (NIR/GREEN)-1 performed better in the site where soil moisture was non-limiting (L).     

Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning

Research into crop growth models at the spatial scale is of great significance for evaluating crop growth, predicting grain yield and studying global climate change. Coupling spatial remote sensing (RS) data can effectively promote the simulation of growth models at spatial scales. However, the integration of RS data and crop models to produce a coupled model based on pixel by pixel requires a large amount of calculations. Simulation zone partitioning is used to separate and cluster the large area into a few relatively uniform zones. Then, the growth model can run on the basis of these units. This method both reflects spatial heterogeneity and avoids repeated simulations of regions with similar attributes, improving the simulation efficiency. In this study, simulation partitioning was performed using soil nutrient indices (organic matter content, total nitrogen content and available potassium content) and corresponding spatial characteristics of wheat growth, as indicated by RS data. A coupled model, integrating RS information and the WheatGrow model, using vegetation indices as the coupling parameters (based on the Particle Swarm Optimization algorithm and PROSAIL model), was developed. The aim was to realize accurate prediction of wheat growth parameters and grain yield at the spatial scale. Good zone partitions were obtained by partitioning with the spatial combination of soil nutrient indices and the wheat canopy vegetation index, calculated during the main growth (jointing, heading and filling) stages. The variation coefficients of each index within individual simulation sub-zones were much smaller than those of the indices across the whole area. An analysis of variance showed that the indices were significantly different between the simulation sub-zones, which indicated that appropriate simulated sub-zones had been defined. The minimum root mean square error of the leaf area index, leaf nitrogen accumulation and yield between the predicted values and the values simulated by the coupled model were 0.92, 1.12 g m^?2, and 409.70 kg ha^?1, respectively, which were obtained when the soil-adjusted vegetation index was used as a partitioning zone and assimilating parameter. These results demonstrated that the coupled model of the crop model and RS data, based on the simulation sub-zones had a good prediction accuracy. The results provide important technical support for increasing model efficiency, when crop models need to be applied at the spatial scale.     

Soil tillage methods by years interaction for dry matter of plant yield of maize (Zea mays L.) using additive main effects and multiplicative interaction model

The objective of this study was to assess soil tillage methods by years interaction for dry matter of plant yield of maize(Zea mays L.) grown in West Poland by the additive main effects and multiplicative interaction model. The study comprised four soil tillage methods, analysed in 12 years through field trials arranged in a randomized complete block design, with four replicates. Dry matter of plant yield of the tested soil tillage methods varied from 86.7 dt ha~(–1)(for no-plough tillage in 2005) to 246.4 dt ha~(–1)(for complete conventional tillage in 2012), with an average of 146.6 dt ha~(–1). In the variance analysis, 49.07% of the total dry matter of plant yield variation was explained by years, 12.69% by differences between soil tillage methods, and 10.53% by soil tillage methods by years interaction. Dry matter of plant yield is highly influenced by soil tillage methods by years factors.