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Analysis of the Spatial and Temporal Variability of Irrigated Maize Yield
Abstract
The quantitative analysis of the yield of seven irrigated plots of land sown with maize was carried out over a period of 3 yr in order to determine the spatial and temporal variability. The methodology used was based on: inter-annual analysis of yield, which quantifies the overall difference in production from 1 yr to the next; and temporal variance, which indicates the variability of yield at a given point over time. The results show that even with irrigated crops, where production factors are generally subject to high degree of control, there is a high degree of inter-annual variability, becoming less marked over time. These results enable an investment risk map associated with a given crop and a given plot to be drawn; they also enable it to be shown that the sum of the yield for stable zones for each field decreases as field area increases, indicating that the greater the length of the centre pivot irrigator used, the lower the temporal stability of yield. The most stable yields are associated with an average distance of about 15 m from the flow lines; finally, the results show that forecasting for future years is extremely difficult owing to the fact that the temporal variability of yield may vary between 1 and 8 t/ha.
... Examples of precision management include variable rate irrigation (VRI), variable rate nitrogen application, variable rate lime application, among many others (Radoglou-Grammatikis et al., 2020). As a result of the developments in PA technologies during the last several decades, the ability to produce more detailed spatial information has increased rapidly and offered a better characterization of the within-field crop and soil variability (Blackmore et al., 2003;Marques da Silva, 2006). The widespread application of automatic guidance systems based on real-time kinematic (RTK) GPS signals allows producers to collect accurate digital elevation data while performing field operations without additional costs. ...
... The widespread application of automatic guidance systems based on real-time kinematic (RTK) GPS signals allows producers to collect accurate digital elevation data while performing field operations without additional costs. Such elevation data enables the generation of high-resolution digital elevation models (DEMs), which are essential for topographic analysis in PA applications (Blackmore et al., 2003; Marques da Silva, 2006; Marques da Silva and Silva, 2008;Vitharana et al., 2008). ...
Topographic variability is one of the main factors that affect soil properties and crop production. In this work, we reviewed the concept and significance of various topographic attributes and their influence on the factors contributing to crop growth and yield, including soil physical, hydraulic, chemical, and biological properties and nutrient distribution as affected by landscape positions. The effects of topography on soil properties and crop growth and yield depend on soil type and depth, climate, cropping system, and management practices. Accurate elevation data and its derived topographic attributes have broad applications in precision agriculture, including site-specific applications of seed, fertilizer, and water, management zone delineation, designing contours or terraces and precision leveling plans, and planning soil and water conservation programs for enhanced profitability. This work revealed the lack of research in application of secondary topographic attributes in agriculture and on-farm research in using topography in site-specific management of crop inputs. Interdisciplinary research efforts is suggested to enhance the understanding and promote application of topographic information in precision agriculture. Furthermore, there are needs to raise the awareness of producers to use the built-in real-time kinematic (RTK) Global Navigation Satellite System (GNSS) technologies to collect accurate three-dimensional data for site-specific management. In addition, this work suggested the need for user-friendly tools to help producers employ the collected topographic data to achieve precision agriculture goals.
... With the exception of southern portions of F28 and F31, yield at the edges of the fields was either consistently low or had a high degree of temporal instability. Previous studies reported similar effect and have attributed this to the operation characteristics of center pivot irrigation systems that cause more run-off water erosion towards the edge of the irrigation system due to higher kinetic energy [6,7]. Consistent with the temporal stability analysis, F35 had the highest yield stability, with approximately 90% of the field area classified as temporally stable (56.1% HS and 33.7% LS); F32 had the lowest yield stability, with 70.6% of the field area classified as unstable (53.2% HU and 17.4% LU). ...
... In these cases, a more appropriate option would be within season site-specific management of crop inputs, such as irrigation, plant growth regulators based on variability in crop growth conditions. Similarly, Blackmore et al. [6] and Silva [7] recommended each growing crop should be managed according to its current needs when yield map trends are not stable. ...
Understanding spatial and temporal variability patterns of crop yield and their relationship with soil properties can provide decision support to optimize crop management. The objectives of this study were to (1) determine the spatial and temporal variability of cotton (Gossypium hirsutum L.) lint yield over different growing seasons; (2) evaluate the relationship between spatial and temporal yield patterns and apparent soil electrical conductivity (ECa). This study was conducted in eight production fields, six with 50 ha and two with 25 ha, on the Southern High Plains (SHP) from 2000 to 2003. Cotton yield and ECa data were collected using a yield monitor and an ECa mapping system, respectively. The amount and pattern of spatial and temporal yield variability varied with the field. Fields with high variability in ECa exhibited a stronger association between spatial and temporal yield patterns and ECa, indicating that soil properties related to ECa were major factors influencing yield variability. The application of ECa for site-specific management is limited to fields with high spatial variability and with a strong association between yield spatial and temporal patterns and ECa variation patterns. For fields with low variability in yield, spatial and temporal yield patterns might be more influenced by weather or other factors in different growing seasons. Fields with high spatial variability and a clear temporal stability pattern have great potential for long-term site-specific management of crop inputs. For unstable yield, however, long-term management practices are difficult to implement. For these fields with unstable yield patterns, within season site-specific management can be a better choice. Variable rate application of water, plant growth regulators, nitrogen, harvest aids may be implemented based on the spatial variability of crop growth conditions at specific times.
... Previous attempts to create paddock stability zones have been restricted to either crop or grassland paddocks, never with crop and pasture sequences simultaneously. A number of researchers have described the creation of stability zones in either crop or pasture paddocks (Blackmore, 2000;Blackmore et al., 2003;Marques da Silva, 2006;Marques Da Silva et al., 2008;Xu et al., 2006). However, all have reported difficulty in establishing a valid approach to determine a "threshold" value for temporal variability. ...
... In this study, a highly managed monoculture in the cropping phase was compared to a largely unmanaged, highly diverse and complex sward of pasture species with uncontrolled animal impact. The creation of stability indices for cropping enterprises is relatively straight forward and has been documented (Basso et al., 2012;Blackmore et al., 2003;Marques da Silva, 2006). There is also usually a reasonable number of years of high resolution crop yield data available. ...
Precision farming technologies are now widely applied within Australian cropping systems. However, the use of spatial monitoring technologies to investigate livestock and pasture interactions in mixed farming systems remains largely unexplored. Spatio-temporal patterns of grain yield and pasture biomass production were monitored over a four-year period on two Australian mixed farms, one in the southwest of Western Australia and the other in southeast Australia. A production stability index was calculated for two paddocks on each farm. An example is given here for one paddock from Western Australia. The stability index described here is unique in that it combines spatial and temporal variation across both cropping and pasture phases. Coefficient of variation in yield was used as the threshold value for determining stability. Production in each stability zone was analysed statistically for consistency and correlation between the cropping and pasture phases. Results indicate that the stability index can be used in mixed farming systems to assist in management decisions and for the paddock described, spatial and temporal variation in production between crop and pasture phases was strongly correlated.
... The mixed-cropping systems are seldom surveyed in the literature addressing the use of multiple-year yield data in spatial and temporal variability assessment, despite the fact that mixed-cropping systems are more common than mono-cropping systems, and they are supported by funding schemes in the European Union and elsewhere in the world. More often, multiple-year yield data of a specific crop are used in spatial and temporal variability assessment [4,40,41,42]. Alternatively, yield data from cropping systems with the same crop type as spring grain crops are also used [43]. ...
Availability of georeferenced yield data involving different crops over years, and their use in future crop management, are a subject of growing debate. In a 9 hectare field in Northern Italy, seven years of yield data, including wheat (3 years), maize for biomass (2 years), sunflower, and sorghum, and comprising remote (Landsat) normalized difference vegetation index (NDVI) data during central crop stages, and soil analysis (grid sampling), were subjected to geostatistical analysis (semi-variogram fitting), spatial mapping (simple kriging), and Pearson's correlation of interpolated data at the same resolution (30 m) as actual NDVI values. Management Zone Analyst software indicated two management zones as the optimum zone number in multiple (7 year) standardized yield data. Three soil traits (clay content, total limestone, total nitrogen) and five dates within the NDVI dataset (acquired in different years) were shown to be best correlated with multiple-and single-year yield data, respectively. These eight parameters were normalized and combined into a two-zone multiple soil and NDVI map to be compared with the two-zone multiple yield map. This resulted in 83% pixel agreement in the high and low zone (89 and 10 respective pixels in the soil and NDVI map; 73 and 26 respective pixels in the yield map) between the two maps. The good agreement, which is due to data buffering across different years and crop types, is a good premise for differential management of the soil-and NDVI-based two zones in future cropping seasons.
... In this complex situation, many studies addressed different methods of delineating site-specific crop management (SSCM) zones within a field, by relating yield data with soil properties and external factors. Da Silva (2006) produced classified zones based on spatio-temporal yield maps. Lark and Stafford (1996) used an unsupervised fuzzy clustering method over multiple years' yield data. Swindell (1997) analyzed the spatial variability by using several crop harvest indices. Fraisse et al. (1999) combined topographical variables and ECa through unsupervised cluster analysis. ...
ABTRACT
Soil and weather data were used to analyse spatio-temporal yield patterns of winter cereals (wheat) and spring dicots (sunflower and coriander) in an 11-ha field in Northern Italy (44.5° N, 12.2° E), during 2010–2014. Three yield stability classes (YSCs) were established over multiple years, based on spatio-temporal characteristics across the field: high yielding and stable (HYS), low yielding and stable (LYS), and unstable. The HYS class (46% of field area) staged a 122% relative yield with low temporal variability. The unstable class (24% of field area) was slightly more productive (83% relative yield) than the LYS class (30% of field area, and 80% relative yield), but less consistent over time. Crop yields evidenced negative correlations with sand content; positive correlations with silt and clay content. Soil properties were quite consistently classified among YSCs: the LYS and unstable classes were associated with higher sand content and lower cation exchange capacity, suggesting that these characteristics lead to fluctuation and depression of final yield. Establishing YSCs based on spatio-temporal yield appears a sound approach to appraise field potential. It results in strategic and tactical decisions to be taken, depending on the profile of spatial and temporal productivity in different field areas.
... High spatial variability is usually found in soils. In consequence, uniform management of fields does not take into account the spatial variability, and it is not the most effective management strategy (Marques da Silva 2006;Moral et al. 2010). Nowadays, farm areas are rapidly increasing in China due to the implementation of the land transfer policy. ...
Precision agriculture has been increasingly practised in recent years. Under precision management, farmland is divided into several management zones to implement different strategies and improve irrigation water productivity and revenues. In this research, six soil properties (silt, sand, soil moisture content, available nitrogen, electrical conductivity and elevation) were selected, and fuzzy c-means clustering was used to delineate management zones. The field was divided into three zones. The differences in the mean of the soil properties among the zones were large and within a zone were small. The coefficient of variation of the properties and yield were also smaller than that before classification. The optimization was carried out by using a genetic algorithm based on the Jensen model. Three objective functions were set as maximum yield, maximum irrigation water productivity (IWP) and maximum revenue, and the weights were kept equal to 1/3. The WHCNS (soil water heat carbon nitrogen simulator) model was used to simulate the maize yield under optimized irrigation schedule for the three management zones and to calculate IWP and revenue. Compared with uniform management, IWP and revenue were increased by 0.6 kg m⁻³ and $61 ha⁻¹, respectively. The optimized irrigation schedule can be used as a reference for the actual irrigation management. It can increase the IWP and revenue under the premise of achieving the target yield. The results show that the method can guide precision agricultural production and management in large-scale farmland.
... By and large, pasture-livestock phases are 'lowinput', where livestock and the pastures they graze are managed less intensively than crops (Bell et al. 2014a;Kirkegaard et al. 2011). Several approaches have been used in the past to identify regions of temporal stability in crops (Blackmore 2000(Blackmore , 2003Diacono et al. 2012;Dobermann et al. 2003;Marques da Silva 2006); and in pastures (Marques da Silva et al. 2008;Serrano et al. 2011Serrano et al. , 2014Schmer et al. 2009;Xu et al. 2006). The assessment of temporal stability is important because it affects the reliability of management zones as a strategy for differential management in crop and pasture phases. ...
While precision agriculture (PA) technologies are widely used in cropping systems, these technologies have received less attention in mixed farming systems. Little is known about the nature, extent, and temporal stability of spatial variability of pastures in mixed farming systems and the feasibility of managing this variability. This paper describes a technique to create a Stability Index based on both crop grain yield and pasture total green dry matter (TGDM) production over time, using high resolution spatial data in two climatic zones of Australia. Four productivity zones were used to characterise the Stability Index: high and stable, high and unstable, low and stable, and low and unstable. Mapping the indices shows the location and size of the spatial and temporal features of each paddock. The features of the stability zones generally corresponded with soil texture classes. Testing the Stability Indices with a Kruskal–Wallis one-way ANOVA showed significantly different medians for high and low production categories for both grain yield and pasture TGDM (p < 0.01). Crop grain yield stability showed significant differences between medians. In pasture TGDM, the differences between stability medians were not significant, but the technique still separated medians into stable and unstable groupings. This production Stability Index has the potential to be used by farmers to manage spatial variability in mixed farming systems by identifying homogenous areas within a paddock for investigation/amelioration and can also separate out areas of either spatial and/or temporal instability for specific management strategies.
... The effect of long-term no tillage on spatial variability of yield of soybean and maize was studied by Vieira et al. (2010). Silva (2006) studied the spatial and temporal variability of irrigated maize yield. Spatio temporal analysis of rice yield variability in two Californian fields was done by Roel and Plant (2004). ...
... Costs are estimated as a percentage of producer price using production costs for the various crops [17] modified by the Cost of Production Formation Index and Capital [15]. The standard deviations of incomes for the various crops are calculated using coefficients of variation for yields [18,19,20,21,22]. The standard deviations of costs for the various crops were estimated using the standard deviation of costs after establishment of oil palm [11]. ...
... Finalmente (7), aproveitando a existência de cartas relativas a dois anos, foi feita uma análise espácio-temporal (Blackmore 2000;Blackmore et al., 2003;Marques da Silva 2006), comparando a carta de tendência espacial com a de estabilidade temporal, de modo a classificar as diferentes zonas da parcela como: "produtividade elevada estável"; "produtividade baixa estável"; e "produtividade instável". Como critério para a definição de elevada e baixa produtividade assim como estabilidade ou instabilidade temporal da produtividade utilizou-se a média das médias e a média das diferenças das produtividades para cada ponto, respectivamente. ...
... Este método procura determinar o melhor arranjo dos dados em diferentes classes através da minimização dos desvios para as médias em cada classe e maximização dos desvios de cada classe para as médias das restantes classes. Finalmente (7), aproveitando a existência de cartas relativas a dois anos, foi feita uma análise espácio-temporal (Blackmore 2000; Blackmore et al., 2003; Marques da Silva 2006), comparando a carta de tendência espacial com a de estabilidade temporal, de modo a classificar as diferentes zonas da parcela como: " produtividade elevada estável " ; " produtividade baixa estável " ; e " produtividade instável " . Como critério para a definição de elevada e baixa produtividade assim como estabilidade ou instabilidade temporal da produtividade utilizou-se a média das médias e a média das diferenças das produtividades para cada ponto, respectivamente. ...
A agricultura confronta-se hoje com o enorme desafio de, num contexto de crescente escassez de recursos, ser capaz de aumentar significativamente a produção agrícola nas próximas décadas, para sustentar o contínuo aumento da população mundial. Em resultado da actuante limitação de expansão da terra arável sob cultivo, o aumento da produção agrícola terá, forçosamente, de se fazer por via do aumento da produtividade. No passado recente, a produtividade agrícola aumentou, essencialmente, como fruto dos ganhos ou avanços no melhoramento genético, na intensificação do uso de factores de produção e na inovação tecnológica. A agricultura de precisão visa, precisamente, ser uma resposta inteligente para todo este problema, propondo-nos, através da incorporação e do uso de algumas inovações tecnológicas, uma via de continuidade para o aumento da produtividade e do uso intensivo de factores. O grande desafio a que a agricultura de precisão pretende responder é o de como produzir mais e melhor com os mesmos ou com menos recursos, concorrendo, em simultâneo, para a redução de impactes ambientais indesejáveis. O aumento da eficiência e da eficácia do uso de factores que está implícito neste desafio, pode, por exemplo, ser conseguido com recurso à combinação das tecnologias de GPS e de taxa de débito variável, o que nos permite, sobretudo em grandes parcelas com solos e fertilidade heterogéneas, efectuar uma distribuição espacial mais precisa, ou seja mais de acordo com necessidades e o potencial de produção específicos de cada posição ou local. Não obstante as teóricas vantagens comparativas da tecnologia de taxa variável, a verdade é que a sua adopção por parte agricultores tem sido reduzida ou nula. Este aparente paradoxo resulta, em boa parte, da inexistência, ou da falta de divulgação, de exemplos concretos que comprovem claramente a vantagem económica da aplicação da tecnologia. Com este trabalho pretendemos divulgar os resultados de um estudo realizado num pivot de milho na região da Golegã, em que se ensaiaram as alternativas de agricultura convencional e de precisão, a propósito da gestão da água e do azoto. As duas alternativas tecnológicas são comparadas através dos resultados alcançados nas vertentes produtividade, nível de utilização de factores e impacte ambiental.
... In a recent past spatial and temporal analyses of yield variability was considered very important in order to delineate areas of stable yield patterns for application of precision farming techniques (Bakhsh et al., 2000). More recently, several authors have found that most spatial variability disappears over time if we consider the average productivity map, consequently yield maps cannot forecast the yield pattern for the following year and crops should be managed in accordance with their needs in real time (Blackmore et al., 2003;Marques da Silva, 2006). ...
Precision agriculture techniques imply a spatial management of fields and to do so a good understanding of the spatial and temporal variability of yield is needed. Average yield data from seven irrigated maize fields were used to study the yield pattern considering the distance of plants to flow accumulation lines. It was found that there is a significant correlation between average yield and distance to flow accumulation lines (DFL). This correlation is best represented by a polynomial function. The most common shape of the yield pattern curve considering the distance to flow accumulation lines shows that there is an increase in average yield with DFL from 0 to 12.5–17.5m. Near the flow lines the average yield presents lower values due to drainage problems causing plant growth problems. It was also observed higher yield variability near the flow lines. For higher distances from the flow lines there is a continuous decrease in average yield due to less water availability and other variations of soil properties.
... The availability of geo-referenced yield data obtained with yield monitors has allowed several researchers (e.g., Blackmore et al., 2003;Marques da Silva, 2006) to make a detailed and precise characterization of yield spatial and temporal variability. Yield variability may be caused by many factors, but one that is most frequently related to yield is topography (Kravchenko and Bullock, 2000). ...
Maize yield data were collected in seven agricultural fields irrigated by centre-pivot irrigation systems, in Southern Portugal, from 2002 to 2004. These data were then correlated with different primary and secondary topographic attributes. The attained correlation coefficients were used to evaluate the relationship between yield spatial variability and each individual topographic attribute. In this three-year period applied water was always lower than crop water requirements. The increase of applied water in 2004 resulted in an improvement in average yield and especially in yield spatial stability. Average yield showed a strong dependency on topography, with high correlation coefficients between yield and elevation and slope. It presented also a high correlation with topographic indices that reflect field water availability, such as the wetness index and distance to flow accumulation lines (DFL). The DFL index was the topographic index with higher correlation coefficients with yield. The negative coefficients of correlation between yield and DFL, attained in most fields in the three-year study period, show that, in general, yield increases with the decrease of DFL, i.e., with the increase of water availability. In undulating land areas flow lines are very abundant, which means that, in these conditions, the DFL index can be a good tool to evaluate yield spatial variability.
The availability of combine yield monitors since the early 1990’s means that long time-series (10+ years) of yield data are now available in many arable production systems. Despite this, yield data and maps are still under-exploited and under-valued by professionals in the agricultural sector. These historical data need to be better considered and analyzed because they are the only audited means by which growers and practitioners can assess the spatio-temporal yield response within a field. When done, time-series of yield maps are mostly processed by classification-based algorithms to generate spatial and temporal yield stability maps or to provide yield or management classes. This work details an alternate segmentation-based methodology to first generate and then characterize contiguous within-field yield zones from historical yield data. It operates on the yield data rather than interpolated yield maps. A seeded region growing algorithm is proposed that enables both the specification of seeds and zone segmentation in a multivariate (multi-temporal yield) attribute space. Novel metrics to assess the yield zoning are proposed that are derived from textural image analysis. The zoning algorithm and metrics were applied to two fields with long time-series (6+ years) of yield data in combinable crops. The two case studies showed that the proposed zone-based approach was effective in delimitating relevant within-field yield zones. The generated zones had differing temporal yield responses between neighbouring zones that were of agronomic significant and interest to the production systems. As this is a first attempt to apply a segmentation algorithm to yield data, areas for future development applications are also proposed.
http://hdl.handle.net/10396/10181
Different techniques are used in Precision Agriculture (PA) to manage the variability in each place of an agricultural field depending on the agronomic characteristics of any crop. Fertilization, planting and pesticide treatments to variable rate, the segmentation of irrigation, varieties or even determining zonal crop yield and traceability management are some of its many applications.
The effective implementation of PA requires the identification of areas with homogeneous characteristics (management zones) which lead to similar potential yield. To do that, new commercial equipments with contact sensors to measure shallow (0-30 cm) and deep (0-90 cm) soil apparent electrical conductivity (ECa) has provided to farmers with a quick and cheap method to obtain that information.
With the aim of characterizing the spatial variability of the main soil physical and chemical properties from ECa data, and using this information to determine the potential management zones, the use of univariate and multivariate geostatistical algorithms and other methods to integrate the obtained information, as the principal component analysis or the Rasch model, can be important tools for the PA. Moreover, when the variables are spatially correlated, as those of interest in agronomic issues, the relationships have to be taken into account. In this sense, the use of conventional regressions, not considering the geographic component of the data, is not adequate. It is necessary to utilize Geographically Weighted Regressions (GWR) which consider the locations to establish the relationships between variables. This allows to categorize the importance of existing relationships at any place of the field and decide which variable is the more important to estimate potential soil fertility.
In consequence, the combination of geostatistical techniques, the Rasch model and GWR constitutes one of the most appropriate and novel way to delimit with a rational basis the more homogeneous areas in an agricultural field.
From results obtained in the works which were carried out in the experimental field, it has been verified that shallow (ECs) and deep (ECd) soil apparent electrical conductivity are adequate variables to model those soil properties which can inform us about its fertility. Particularly, it has been obtained that ECs is the more adequate to perform the temporal variability analysis of apparent electrical conductivity and establish the relationship with the sunflower crop in a dryland field. Moreover, it is closely related to the main soil properties (clay and sand content, cation exchange capacity and pH).
The GWR method can be used to interpret the spatial variability between different soil properties across the experimental field as the spatial dependency between soil variables is very important.
From the study about the crop yield temporal variability in the same field it is evident that a good relationship between years exists, despite some differences due to climate or wild birds attacks. It has been also observed that ECs values are more stable than crop yield values, so ECs is a proper index to estimate potential soil fertility.
Finally, the high correlation between results obtained by combining principal component analysis and unsupervised classification with those obtained by the Rasch model to generate management zones allows us to conclude that this method can also generate reliable results.
This paper describes a unique calculation that can be applied to historical yield maps of a crop type in a field to yields zones of yield potential performance. Potential performance is in the form of probabilities that a given cell will over-perform the average yield. Those cells that are 100% likely to over-perform the average have done so in each of the historical yield maps used to form the zone map. These maps can be used to guide soil sampling and to form a crude variable rate prescriptions.
There is emerging interest in evaluating the uncertainty of agricultural production to support the production process and for guidance in decision making. The main objective of this work was to estimate the spatial and temporal maize yield uncertainty using stochastic simulation techniques to reduce the economic risk considering the producer risk profile and the international prices of maize and inputs. The results showed that (i) the class yield percentage variation in yield stochastic simulation depends on the sampling density; (ii) higher sampling densities promote an overestimation of low and high yield values compared to those of real yield data; (iii) reducing sampling density promotes the low and high values of overestimation reduction while increasing the central classes values compared to those of real yield data; (iv) the ideal point density for yield stochastic simulation is approximately 65 points/ha; (v) in Mediterranean environments, more than 3-4 years’ worth of real yield data considered as a whole do not seem to improve the parcel level of confidence when cropping irrigated maize; and (vi) the number of equi-probable surfaces that were generated by sequential Gaussian simulation helped to calculate the yield class uncertainty and permitted the study of class yield probabilities for a particular position of the parcel and, therefore, to manage the yield risk and support future decisions. The approach that is presented in this paper may increase prior knowledge of agricultural parcel behavior in the absence of multi-year data, thereby increasing the possibility of reducing economic risks.
Recent farming machinery provides multiple ways to gather spatial information about soil and cultivated crop properties. However, collected information can not be used straight forward for decision making purposes due to complex relationships between crop growth, weather and soil properties.
A quantitative analysis of yield data from three fields over three years was carried out to investigate the spatial and temporal trends. At first it was studied if varying soil water retention properties reflect the observed within-field yield variation of small grain cereals and whether the variations in weather conditions between growing seasons affect the within-field yield variation. Secondly, the potential biomass accumulation in existing soil with varying precipitation was simulated and analysed. The analysis results of the observed biomass yield and simulated biomass yield were compared.
The variation in soil water retention properties was found to have an effect to yield variation. However, soil properties alone were concluded not to be enough to predict the yield and the yield variation correctly in case of high variations. There were other factors affecting the observed yield variation, e.g. lodging, cold summer, extremely high precipitation and the field topography. The prediction of site-specific biomass accumulation solely by soil properties was concluded to be impossible. Therefore crop observations were found necessary among the knowledge of soil properties in order to simulate the potential biomass accumulation for precision farming purposes.
Multispectral aerial and satellite remote sensing plays a major role in
crop yield prediction due to its ability to detect crop growth
conditions on spatial and temporal scales in a cost effective manner.
Many empirical relationships have been established in the past between
spectral vegetation indices and leaf area index, fractional ground
cover, and crop growth rates for different crops through ground
sampling. Remote sensing-based vegetation index (VI) yield models using
airborne and satellite data have been developed only for grain crops
like barley, corn, wheat, and sorghum. So it becomes important to
validate and extend the VI-based model for tuber crops like potato,
taking into account the most significant parameters that affect the
final crop yield of these crops. This research involved developing and
validating yield models for potato crop in southern Idaho fields using
high-resolution airborne and satellite remote sensing. High-resolution
multispectral airborne imagery acquired on three dates throughout the
growing season in 2004 was used to develop a VI-based statistical yield
model by integrating the area under the Soil Adjusted Vegetation Index
(SAVI) curve. The model was developed using hand-dug samples collected
in two center pivots based on soil variability and crop growth patterns
to account for variability in the leaf area duration and yields. The
three-date Integrated SAVI (ISAVI) model developed was then validated
using 2005 spot yield samples collected from two center pivot fields and
also tested for 2004 and 2005 whole field data over dozens of center
pivot fields. The three- date model was applied using 2004 and 2005
satellite images and tested. The eight-date ISAVI yield model was also
extended to satellite images to estimate the potato yield. The overall
yield estimation using the eight-date ISAVI model was better than the
three-date model as the image inputs covered the complete growth cycle
of the crop from emergence to harvest. Actual Evapotranspiration was
also used as another independent variable in the model to improve the
yield predictions. The actual ET was calculated using canopy reflectance
based crop coefficient method for all the spot yield locations in 2004
and regressed with actual yield. Both actual yield and ET correlated
very well. Multiple linear regression analysis was performed using two
independent variables, namely, ISAVI and actual ET to predict the actual
potato yield. The results showed a significant improvement in the
correlation and the new model developed was validated using 2004 and
2005 whole field data. The results showed a reasonable RMSE and low MBE
as well as a good linear correlation for both the years and a great
improvement over yield estimated using only the three-date ISAVI in the
simple linear regression model. A spatial variability analysis was also
performed at different scales using airborne and satellite images to
understand the typical spatial correlation within potato fields.
Yield monitors commonly show that there are very large yield differences within a field which often differ from year to year. Because our ability to estimate reductions in growth and to quantify yield losses resulting from complex interactions and multiple stresses is limited, it does not appear feasible to analyse yield variability using a point to point strategy. For a farmer it is important to select parcels of land, or parts of a parcel, with a high yield probability. To analyse the high yield probability zones the Rasch model is used considering a multi-temporal yield data set. The Rasch measure for multi-temporal yield data makes it possible to place on a continuum axis the yield samples considered in terms of annual yield and vice versa. Using the Rasch measurement one can produce yield potential probabilistic maps taking into account each sample coordinate. From a quantitative point of view it is possible to find yield samples that do not support the model, or which do not reach the expected levels. Positive and negative mismatches can be analysed individually or according to a particular year yield. Thus, the Rasch model makes it possible to systematise the data, making it an effective tool for making appropriate decisions regarding areas with higher yield performance and greater stability over time. Also, it makes it possible to compare the yields of different samples and provide appropriate measures to correct, differentially, samples that obtained different inadequate levels.
The crop growth is highly dependent on growth conditions which vary from year to year making precision farming challenging. In the present paper was first investigated whether varying soil physical properties reflect the within-field yield variation of small grain cereals and how do the variations in weather conditions between growing seasons affect the within-field yield variation. Secondly, the potential biomass accumulation of the crop in existing soil and weather conditions was simulated. The simulated and experimentally based site-specific total biomasses were compared in order to find out whether the soil data explains the observed variations in yield.
Yield data over 6 years (1993–1998) were investigated for spatial and temporal trends from a 7-ha field growing winter wheat and oil seed rape. The data were combined into two maps, which characterised the spatial and temporal variability recorded over those years. Techniques were developed to show the maps in either the single crop form for winter wheat, or multiple crops that included the oil seed rape data. The two maps were then combined into a single classified management map, which denoted three categories, each with different characteristics that can have an impact on the way the field is managed. These categories were; high yielding and stable, low yielding and stable, and unstable. The spatial and temporal trends in the single crop were more stable than those in the multiple crops. In percentage terms, with a single crop, the proportions of these three classes were 55, 45 and 0%, respectively. For the multiple crops, the proportions were 58, 39 and 3%, respectively. The economic significance of these areas was assessed by the production of a gross margin map and further analysis showed that the categories returned 741, 691 and 644 £/ha, respectively.
A quantitative analysis of yield data from four fields over 6 years was carried out to identify the spatial and temporal trends. The methodology was modified from previous work to separate the temporal effects into two parts; the inter-year offset and the temporal variance. The inter-year offset quantifies the overall differences in yield between 1 year and the next, whereas the temporal variance indicates the amount of change at a particular point over time. Results from these fields show that the significant spatial variability found within each individual yield map cancelled out over time, leaving a relatively homogenous spatial trend map. The implications of these findings are that each field should be managed according to the current years’ conditions.
The effect of droplet impact on the infiltration characteristics of a Warden silt loam soil were investigated in the laboratory. The hydraulic resistance of the seal formed on the soil surface by impacting water droplets was found to increase with increasing droplet kinetic energy per unit soil surface area (KE//a). Experimental data also showed that the depth of water infiltrated prior to surface ponding decrease with increasing application rate (rainfall intensity), kinetic energy per water droplet (KE//d), and water droplet enerty flux (DE//f). A unique relationship between DE//f and the depth infiltrated prior to ponding was noted for the Warden silt loam. A procedure for determining the depth of water that can be applied to the Warden silt loam without the potential for runoff based on this relationship and an equation for computing DE//f was presented.
Developments in the area of Precision Farming have led to many of the hardware requirements being available. The target of further activities is to develop an interactive information system which supports the use of this hardware. ISO 11783 Part 9 (1994) is a data dictionary which specifies mobile data communications in agriculture. In addition, Hansen (1994) describes an interface for the communication between management information systems and mobile process control systems.
Alongside this kind of interactive information flow there appears to be a second kind of interactive information flow which takes into account the data transfer between information systems on farm computers and farm managers. Since an information system has to support farm managers in their decision making process the information has to be customised. Decision analysis must be carried out as part of the development of an information system. Farm managers must be able to use this information system for: (i) Data entry, organisation and storage; (ii) Data analysis and interpretation; (iii) Data integration and implementation.
For the data entry a data dictionary was developed, which defines a structure for naming files according to their content. The files are organised in a database and stored in a normal Directory / File structure. Due to the fact that spatial data relies on visual analysis, guidelines for map presentation were established, as they provide a straight forward visual comparison. In order to perform automated map interpretation, AI tools have to be employed. For the integration and implementation of spatial and temporal data, methodologies are rare and need further development. In this study four different yield maps from one field were combined. The authors conclude that further developments especially in the software area have to consider farm managers decision making processes.
In 2003, soil and winter wheat crop information were gathered on an Arenic Cambisol field dominated by sandy silt loam and sandy loam soils according to the Belgium soil classification. Crop information consisted of spectral reflection measurements taken in May and yield mapping in August. Soil information, obtained after harvest, included moisture content and dry bulk density. The soil and crop information of this field were analysed to assess the effect of the measured soil dry bulk density on the crop and to test if the measured soil bulk density could contribute to better field management. Where dry soil bulk density was above 1·6 Mg/m3, the yield was limited, otherwise, no relation was observed between the yield and dry soil bulk density. Different methods of defining management zones based on soil and crop information were compared. Fuzzy clustering was used to make soil management zones based on the soil data only, but these clusters did not explain much of the yield variability. Another set of management zones was defined based on soil information and crop. These clusters provided a better description of the yield variation. A low-yielding area could be delineated related to high soil bulk density. Outside this area, yield was more affected by soil moisture variability and other factors.
Automated pattern recognition by multivariate clustering is proposed as a tool for interpreting the temporal and spatial variation of crop yield. A trial showed that some general patterns of season-to-season variation could be identified and related to soil variability. Such a procedure would be a useful first step in the investigation of sources of yield variation, leading to interpretation and (possibly) spatial variation of inputs.
Tillage-induced soil erosion or redistribution increases spatial variation of several soil properties and often reduces the productive capacity of soil resources. Our objectives were to identify the extent of this type of erosion by observing the changes in soil morphological properties in the field and analysing its possible effects on soil productivity. The study was initiated in 2001 and conducted at two irrigated sites located approximately at Terena, Alandrol, 80 km east of Évora, Portugal. They were planted to corn (Zea mays L.) during this study, but have a long history of agricultural use with a trend toward increasing intensity in recent years. Soils in the field studies are classified mainly as Calcaric Regosols, Calcaric Cambisols, Luvisols and small areas of Fluvisols. The amount of erosion was estimated by simulation and verified by describing the lithology and measuring soil carbonates. The presence of carbonates in the superficial Ap horizons of soils that were previously devoid of this compound, provide evidence of soil redistribution: (1) in soils derived from calcareous parent material, this is the result of a re-carbonation process; (2) in soils derived from non-calcareous parent material the presence of carbonates in the superficial Ap horizons results from a carbonation process. On both sites, A and B, approximately 17% of the soils sampled were either carbonated or re-carbonated. Carbonation and re-carbonation of soil profiles confirmed that tillage had redistributed the soil-ploughing layer over time. Decreased corn yield was also observed as slope increase. If current agricultural practices are continued in this area, a decrease in soil quality and maximum yield on higher slopes can be expected.
Many yield maps exhibit systematic errors that attenuate the underlying yield variation. Two errors are dealt with in detail in this paper: those that occur when the harvester has a narrow finish to a land and those that occur when the harvester is filling up at the start of a harvest run. The authors propose methods to correct or remove erroneous data by the use of an expert filter, or alternatively use of an interpolation technique called potential mapping.
Experiments at two sites growing winter wheat show that in order to manage a wheat canopy more effectively, the use of specific remote sensing techniques both to monitor crop canopy expansion, and to determine variable nitrogen applications at key timings is required. Variations in seed rate were used to achieve a range of initial crop structures, and treatments were compared to standard farm practice. In the first year, the effect of varying seed rate (250, 350 and 450 seeds m−2) on crop structure, yield components and grain yield, was compared to the effects of underlying spatial variation. Plant populations increased up to the highest rate, but shoot and ear populations peaked at 350 seeds m−2. Compensation through an increased number of grains per ear and thousand grain weight resulted in the highest yield and gross margin at the lowest seed rate. In later experiments, the range of seed rates was extended to include 150 seeds m−2, each sown in 24 m wide strips split into 12 m wide halves. One half received a standard nitrogen dose of 200 kg [N] ha−1, the other a variable treatment based on near ‘real-time’ maps of crop growth. Both were split into three applications, targeted at mid-late tillering (early March), growth stages GS30-31 (mid April) and GS33 (mid May). At each timing, calibrated aerial digital photography was used to assess crop growth in terms of shoot population at tillering, and canopy green area index at GS30-31 and GS33. These were compared to current agronomic guidelines. Application rates were then varied below or above the planned amount where growth was above- or below-target, respectively. In the first field, total nitrogen doses in the variable treatments ranged from 188 to 243 kg [N] ha−1, which gave higher yields than the standards at all seed rates in the range 0·36–0·78 t ha−1 and gross margins of £17 to £60 ha−1. In the second field, variable treatments ranged from 135 to 197 kg [N] ha−1 that resulted in lower yields of −0·32 to +0·30 t ha−1. However, in three out of the four seed rates, variable treatments produced higher gross margins than the standard, which ranged from £2 to £20 ha−1. In both fields, the greatest benefits were obtained where the total amount of applied nitrogen was similar to the standard, but was applied variably rather than uniformly along the strips. Simple nitrogen balance calculations have shown that variable application of nitrogen can have an overall effect on reducing the nitrogen surplus by one-third.
Use of fuzzy mapping to extract management zones from yield maps Integration of yield data from several years into a single map
- B Brouillard
- M Csae
- Scgr
- Mansonville
- Canada Qc
- B Panneton
- M Brouillard
- Piekutowski
B; Brouillard M (2001). Use of fuzzy mapping to extract management zones from yield maps. AIC 2002, CSAE/SCGR, Mansonville, QC, Canada Panneton B; Brouillard M; Piekutowski T (2001). Integration of yield data from several years into a single map. In: Third European Conference on Precision Agriculture (Grenier G;
Mapping the spatial variability in the yield potential of arable land through GIS analysis of sequential yield maps Water droplet impact and its effect on infiltration
- Agro Montpellier
Agro Montpellier, Mont-pellier Swindell J (1997). Mapping the spatial variability in the yield potential of arable land through GIS analysis of sequential yield maps. In: First European Conference on Precision Agriculture (Stafford J V, ed), pp 827–834. BIOS Scientific publishers, Warwick UK Thompson A L; James L G (1985). Water droplet impact and its effect on infiltration. Transactions of the ASAE, 28(5), 1506–1510
Remedial correction of yield map data World Reference Base for Soil Resources Importance of spatial and temporal soil water variability for nitrogen management decisions
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B S; Moore M R (1999). Remedial correction of yield map data. Precision Agriculture Journal, 1, 53–66 FAO, ISRIC, ISSS (1998). World Reference Base for Soil Resources. World Soil Resources Report No.84. Food and Agriculture Organization of the United Nations, Rome, 88pp Geesung D; Gutser R; Schmidhalter U (2001). Importance of spatial and temporal soil water variability for nitrogen management decisions. In: Third European Conference on Precision Agriculture (Grenier G; Blackmore B S, eds), pp 659–664.
World Reference Base for Soil Resources Importance of spatial and temporal soil water variability for nitrogen management decisions
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FAO, ISRIC, ISSS (1998). World Reference Base for Soil
Resources. World Soil Resources Report No.84. Food and
Agriculture Organization of the United Nations, Rome,
88pp
Geesung D; Gutser R; Schmidhalter U (2001). Importance of
spatial and temporal soil water variability for nitrogen
management decisions. In: Third European Conference on
Precision Agriculture (Grenier G; Blackmore B S, eds), pp
659–664. Agro Montpellier, Montpellier
0Á62
2003, 2004
2002
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2002
2003
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2002
2004
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2003
2004
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2002, 2003
2004
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2002, 2004
2003
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Table 3 (continued )
Consistency and change in spatial variability of crop yield over successive seasons: methods of data analysis Interactions between farm managers and information systems with respect to yield mapping Soil carbonation processes as evidence of tillage-induced erosion
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Use of fuzzy mapping to extract management zones from yield maps Integration of yield data from several years into a single map Mapping the spatial variability in the yield potential of arable land through GIS analysis of sequential yield maps
- B Panneton
- M Brouillard
- Csae
- Scgr
- Qc Mansonville
- Canada Panneton
- B Brouillard
- M Piekutowski
Panneton B; Brouillard M (2001). Use of fuzzy mapping to
extract management zones from yield maps. AIC 2002,
CSAE/SCGR, Mansonville, QC, Canada
Panneton B; Brouillard M; Piekutowski T (2001). Integration
of yield data from several years into a single map. In: Third
European Conference on Precision Agriculture (Grenier G;
Blackmore B S, eds), pp 73–78. Agro Montpellier, Mont-
pellier
Swindell J (1997). Mapping the spatial variability in the yield
potential of arable land through GIS analysis of sequential
yield maps. In: First European Conference on Precision
Agriculture (Stafford J V, ed), pp 827–834. BIOS Scientific
publishers, Warwick UK
Thompson A L; James L G (1985). Water droplet impact and
its effect on infiltration. Transactions of the ASAE, 28(5),
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World Reference Base for Soil Resources
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FAO, ISRIC, ISSS (1998). World Reference Base for Soil
Resources. World Soil Resources Report No.84. Food and
Agriculture Organization of the United Nations, Rome,
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Importance of spatial and temporal soil water variability for nitrogen management decisions
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Schmidhalter U (2001). Importance of
spatial and temporal soil water variability for nitrogen
management decisions. In: Third European Conference on
Precision Agriculture (Grenier G; Blackmore B S, eds), pp
659-664. Agro Montpellier, Montpellier
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Consistency and change in spatial variability of crop yield over successive seasons: methods of data analysis
- J Stafford
Stafford J V (1996b). Consistency and change in
spatial variability of crop yield over successive seasons:
methods of data analysis. In: Third International Conference on Precision Agriculture (Robert P C; Rust R H;
Use of fuzzy mapping to extract management zones from yield maps
- M Brouillard
Brouillard M (2001). Use of fuzzy mapping to
extract management zones from yield maps. AIC 2002,
CSAE/SCGR, Mansonville, QC, Canada
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- B Panneton
- M Brouillard
- T Piekutowski
Panneton B; Brouillard M; Piekutowski T (2001). Integration
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European Conference on Precision Agriculture (Grenier G;
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Swindell J (1997). Mapping the spatial variability in the yield
potential of arable land through GIS analysis of sequential
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Importance of spatial and temporal soil water variability for nitrogen management decisions
- Geesung