Specific Crop Management Research Articles

High Efficient IoT based modern soil composition identifier for smart yield farming

In Systematic agriculture the accurate readings of soil nutrients are needed, which includes specific crop management. The nutrients applications are adjusted accordingly on requirement basis. The fertility of the soil decides the essential amount of nutrients and water adequacy for proper yield. In soil chemical testing, analytical laboratories provide insufficient data, only few testing are carried out and other essentials were ignored. To enhances from this, our idea deals with complete soil.

Correlation of gaseous emissions to water stress in tomato and maize crops: From field to laboratory and back

The application of Site Specific Crop Management consists in the knowledge of the soil variability. In particular, for a sustainable water management is fundamental to obtain differential responses in terms of selective irrigation, analyzing and evaluating the water content of the soil or the water requirement of the plants. The innovative contribution of this research lies in designing, developing and validating a technology platform consisting of a hardware for monitoring gaseous emissions, such as Volatile Organic Compounds (VOCs), from the soil-plant-atmosphere system of intensive crops, i.e., tomato and maize. In order to evaluate the water content of these systems, we analyzed experimental data acquired in-situ by portable sensing units based on Metal-OXide (MOX) gas sensors, thus comparing the results with meteo-sat data and farming operations (e.g. irrigations, rainfalls or pesticide-based treatments). The experimentation has proved a dependence of gaseous emissions on the hydric/metabolic status of the plants together with a correlation between sensor signals collected and significant events for the crops.

Characterizing Brazilian soybean-growing regions by water deficit patterns

The use of specific crop management in a growing region according to its climate can reduce the yield gap caused by water deficit, the main cause of soybean yield gap in Brazil. Therefore, this study aimed to characterize the Brazilian soybean-growing regions based on attainable yield and water deficit patterns. The attainable yields were estimated by the crop model DSSAT-CROPGRO-Soybean, considering six sowing dates, three soil types and 33 growing seasons (1980–2013), using gridded weather data with 453 locations. The homogenous regions were defined by the cluster analysis, based on attainable yield. The water deficit pattern (WDP) was identified by a simulated 5-day running average of water stress index (WSPD) during the growing seasons across different planting dates and soil types, using the cluster analysis for each homogenous region. The attainable yield varied considerably between environments, from 0 to 3400 kg ha−1. Ten environmental zones were obtained, where locations in the same zone indicated the same level and variability of the attainable yield across sowing dates, soils and growing seasons. The highest attainable yield (> 2800 kg ha−1) occurred in the central-western to northern regions (zones 1, 2 and 3), covering most soybean growing areas in Brazil. The lowest attainable yield was observed in the extreme South (zone 6) and East border (zone 7) (< 1170 kg ha−1). The highest attainable yield variability occurred in northern/northeastern (zones 4 and 5), eastern (zone 8) and southern (zones 9 and 10) regions. Three WDP were obtained in each environmental zone, which differed in frequency, level and moment of occurrence during the soybean growing season. Sowing dates and soil types contributed to defining the frequency of each WDP. The homogenous zones and WDP demonstrate that attainable yield and yield gap by water deficit can differ significantly across Brazilian soybean-growing regions. These patterns could be used to improve crop management, such as sowing dates, preferential soils, and irrigation requirements, as well as breeding strategies to deal with water deficit, namely deeper root systems, stomatal closure by decreased water content in the soil and drought tolerance of nitrogen fixation.

Establishing modalities for commercial-based variety evaluation and review of relative cane variety composition in sugarcane plantations

Coordinated commercial and trial based variety evaluation is compulsory to avoid sugarcane growers’ unexpected loss due to the pre- and post release variety performance discrepancies. This investigation was conducted to establish modalities for commercial based variety evaluation, estimating the general performance (Gp) and relative variety composition or proportion (RCV) of commercial varieties cultivated in Ethiopia. The cane, sugar and estimated recoverable sucrose yield (ERS) data of commercial varieties obtained from agricultural and factory operations datasets and the survey data were included and analyzed. Three modalities were established for commercial based variety evaluation, estimating GP and RVC of sugarcane varieties. Results from the analysis of variance revealed the existence of significant and complex interactions among the variety, crop cycle, cutting and harvest age effects. Its significance demonstrated the importance of specific crop management for a specific sugarcane variety cultivated in the respective sugarcane plantation and demands the revision of the existing crop management practices in all sugarcane plantations. Generally, the established models were adequate to evaluate variety yield performance, estimate GP and RVC of the varieties evaluated. The models developed to estimate the GP and RCV of varieties in a specific sugarcane plantation were original and are the major innovative parts of this investigation. The serious variety ranking changes across locations highlighted the existence of substantial genotype x environment interaction and signals the long year cultivated sugarcane varieties had specific environmental domains in Ethiopia. The inclusion of survey data (growers’ preference) in the present investigation was much helpful in the process of identifying appropriate variety rankings. Hence, we recommend active participations of sugarcane growers in future commercial and trial based variety evaluations. Moreover, we suggested that future research efforts involving commercial and trial based variety evaluation to adopt the established modalities as a guide to assure efficient and coordinated sugarcane variety evaluation program in Ethiopia.

Soil-plant transfer models for metals to improve soil screening value guidelines valid for São Paulo, Brazil.

In Brazil, there is a lack of combined soil-plant data attempting to explain the influence of specific climate, soil conditions, and crop management on heavy metal uptake and accumulation by plants. As a consequence, soil-plant relationships to be used in risk assessments or for derivation of soil screening values are not available. Our objective in this study was to develop empirical soil-plant models for Cd, Cu, Pb, Ni, and Zn, in order to derive appropriate soil screening values representative of humid tropical regions such as the state of São Paulo (SP), Brazil. Soil and plant samples from 25 vegetable species in the production areas of SP were collected. The concentrations of metals found in these soil samples were relatively low. Therefore, data from temperate regions were included in our study. The soil-plant relations derived had a good performance for SP conditions for 8 out of 10 combinations of metal and vegetable species. The bioconcentration factor (BCF) values for Cd, Cu, Ni, Pb, and Zn in lettuce and for Cd, Cu, Pb, and Zn in carrot were determined under three exposure scenarios at pH 5 and 6. The application of soil-plant models and the BCFs proposed in this study can be an important tool to derive national soil quality criteria. However, this methodological approach includes data assessed under different climatic conditions and soil types and need to be carefully considered.

Exploration of ISE Sensor Embedded in Garden Weed Block Fabric for Continuous Soil Nitrate Sensing

The objective of this research was to explore novel sensing apparatus for quantifying spatial variation of leached nitrate in soil as well as serve as a method for site specific crop management. There is a need to develop sensors that can more accurately and successfully facilitate site specific soil management for monitoring nutrients and pollutants. The dominantly used sensing methods, both in field and in lab, can require lengthy time when analyzing samples and are costly. Therefore, the objective of this research was to develop a sensor on a water permeable weed barrier fabric used in garden bed soil to allow continuous monitoring of nitrate levels. An electrically conductive fabric material was prepared by applying conductive coatings onto the porous material. Nitrate doped polypyrrole (PPy(NO3 -)) electrodes were used as sensing membranes and proved to have high selectivity towards nitrate ions. The electropolymerization protocols and thickness of the polymer layer were optimized (monomer concentration, deposition time, and scan rate) to synthesize the ion selective electrode (ISE) for nitrate determination. The ISE sensor provides a wide dynamic range from 0.008 mg/L to 849.9 mg/L for nitrate ions in real soil solution samples. Overall, the sensing apparatus proves to successfully detect nitrate ions in soil, sand, and solution.

Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing

Intensive farming on land represents an increased burden on the environment due to, among other reasons, the usage of agrochemicals. Precision farming can reduce the environmental burden by employing site specific crop management practices which implement advanced geospatial technologies for respecting soil heterogeneity. The objectives of this paper are to present the frontier approaches of geospatial (Big) data processing based on satellite and sensor data which both aim at the prevention and mitigation phases of disaster risk reduction in agriculture. Three techniques are presented in order to demonstrate the possibilities of geospatial (Big) data collection in agriculture: (1) farm machinery telemetry for providing data about machinery operations on fields through the developed MapLogAgri application; (2) agrometeorological observation in the form of a wireless sensor network together with the SensLog solution for storing, analysing, and publishing sensor data; and (3) remote sensing for monitoring field spatial variability and crop status by means of freely-available high resolution satellite imagery. The benefits of re-using the techniques in disaster risk reduction processes are discussed. The conducted tests demonstrated the transferability of agricultural techniques to crisis/emergency management domains.

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008–2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

Potential role of compost and green manure amendment to mitigate soil GHGs emissions in Mediterranean drip irrigated maize production systems

Organic fertilization can preserve soil organic matter (SOM) and is foreseen as an effective strategy to reduce green house gases (GHGs) emissions in agriculture. However, its effectiveness needs to be clarified under specific climate, crop management and soil characteristics. A field experiment was carried out in a Mediterranean drip irrigated maize system to assess the pattern of soil CO2 and N2O fluxes in response to the replacement of a typical bare fallow–maize cycle under urea fertilization (130 kg N ha−1 y−1) (CONV) with: (i) bare fallow-maize cycles under two doses of compost (COM1 and COM2, 130 and 260 kg N ha−1 y−1, respectively) and (ii) a vetch-maize cycle, with vetch incorporation as green manure (130 kg N ha−1 y−1) (GMAN).Along the maize period (MP), reduced daily N2O emissions were detected in organic treated soils compared to CONV, mainly in the first stages of the cultivation, thanks to the slow release of available nitrogen from the organic substrates. Cumulative N2O fluxes (kg N2O-N ha−1) in MP scored to 0.24, 0.14, 0.12 and 0.085 for CONV, COM1, COM2 and GMAN, respectively, with significantly lower emissions in GMAN respect to CONV. CO2 fluxes partially reflected the ranking observed for maize yields, with cumulated values (Mg CO2-C ha−1) of 2.2, 1.5, 2.1, 2.1 for CONV, COM1, COM2 and GMAN, respectively, and significantly lower in COM1 respect to the other treatments.During the fallow period (FP), compared to CONV (0.77 Mg CO2-C ha−1 and 0.25 kg N2O-N ha−1), enhanced GHG fluxes were detected in COM treatments (about 0.90 Mg CO2-C ha−1 and 0.37 kg N2O-N ha−1, as averaged values from COM1 and COM2), likely driven by the slow prolonged mineralization of the added organic matter. GMAN showed comparable CO2 (0.82 Mg CO2-C ha−1) and N2O emissions (0.30 kg N2O-N ha−1), in consequence of restrained post-harvest residual N coupled with the counteracting effect of vetch uptake.Respect to the total yearly GHG emissions in CONV (about 194 kg CO2 eq ha−1 y−1), the overall results showed commensurate slightly higher GWP in COM treatments (+11% as averaged value from COM1 and COM2). The yield-scaled global warming potential (GWP) resulted 60% higher and nearly doubled for COM2 and COM1 respectively, according to the lower COM yields, markedly dampening at halved compost dose. GMAN appeared the best performing organic treatment, with lower GWP (−27%) and competitive yields respect to CONV.All treatments showed N2O emission factors consistently lower compared with the default IPCC 1% value.

Agricultural Phosphorus Balance Calculator: A tool for watershed planning

Best management practices implemented to minimize agricultural phosphorus (P) water quality impacts typically focus on retaining nutrients rather than improving P use efficiencies. Although it is now well understood that long-term reduction of P loadings requires achieving a watershed level P balance, this concept is generally not incorporated in watershed planning. This paper develops a detailed P balance for the Albert Lea Lake watershed, a high production agricultural watershed in south-central Minnesota, United States. Watershed specific crop and livestock management data were acquired through personal interviews, surveys, feedlot permits, and site visits. These data, together with published resources, were used to develop an open-source Agricultural P Balance Calculator to compute P use efficiencies of livestock operations, crop production, and collectively, the watershed9s agricultural system. In 2010, the P use efficiency was 1.7, indicating that more P was being exported from the watershed as agricultural products than imported, implying that crops were “mining” P from watershed soils. The Agricultural P Balance Calculator enables users to identify production areas where P use efficiency strategies could be incorporated into conservation implementation planning, thereby enhancing prospects for meeting P reduction goals.

Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

Precision Agriculture (PA) recognizes and manages intra-field spatial variability to increase profitability and reduced environmental impact. Site Specific Crop Management (SSCM), a form of PA, subdivides a cropping field into uniformly manageable zones, based on quantitative measurement of yield limiting factors. In Mediterranean environments, the spatial and temporal yield variability of rain-fed cropping system is strongly influenced by the spatial variability of Plant Available Water-holding Capacity (PAWC) and its strong interaction with temporally variable seasonal rainfall. The successful adoption of SSCM depends on the understanding of both spatial and temporal variabilities in cropping fields. Remote sensing phenological metrics provide information about the biophysical growth conditions of crops across fields. In this paper, we examine the potential of phenological metrics to assess the spatial and temporal crop yield variability across a wheat cropping field at Minnipa, South Australia. The Minnipa field was classified into three management zones using prolonged observations including soil assessment and multiple year yield data. The main analytical steps followed in this study were: calculation of the phenological metrics using time series NDVI data from Moderate Resolution Imaging Spectroscope (MODIS) for 15 years (2001-2015); producing spatial trend and temporal variability maps of phenological metrics; and finally, assessment of association between the spatial patterns and temporal variability of the metrics with management zones of the cropping field. The spatial trend of the seasonal peak NDVI metric showed significant association with the management zone pattern. In terms of temporal variability, Time-integrated NDVI (TINDVI) showed higher variability in the “good” zone compared with the “poor” zone. This indicates that the magnitude of the seasonal peak is more sensitive to soil related factors across the field, whereas TINDVI is more sensitive to seasonal variability. The interpretation of the association between phenological metrics and the management zone site conditions was discussed in relation to soil-climate interaction. The results demonstrate the potential of the phenological metrics to assess the spatial and temporal variability across cropping fields and to understand the soil-climate interaction. The approach presented in this paper provides a pathway to utilize phenological metrics for precision agricultural management application.

Application of an original soil tillage resistance sensor in spatial prediction of selected soil properties

Horizontal and vertical variability of soil properties, temporal dynamics, and the complexity of monitoring and prediction processes are the limiting factors for full implementation of all the postulates of site specific crop management. The aim of this research was to prove the possibilities for utilization of original soil tillage resistance measuring device in spatial assessment of some physical properties of soil.The tillage resistance measurement and soil evaluation were performed in 2012 on a field of 0.78ha, with calcic chernozem type of soil. The year before, the field was divided into three equal plots where three types of tillage were applied (moldboard plowing, disc harrowing and chisel plowing) in order to increase spatial soil differences and to ensure more reliable testing. Soil physical properties were observed at 30 points that were arranged on the nodes of a rectangular grid all over the field along with their geopositioning. Soil tillage resistance was measured in real time and space, during 30 passes that were made on the entire field by a standard tractor and plow.The tillage management applied in 2011 resulted in the differences of some soil parameters. Significant differences between the field plots arose with respect to cone index, moisture content and soil tillage resistance. However, no differences arose regarding the texture (sand, silt+clay content) and bulk density. The correlation analysis showed a high positive correlation between soil resistance and cone index (R=0.82) and a negative correlation between soil resistance and moisture content (R=−0.41). Fitting of variograms in the modeling of spatial correlation gave the highest prediction error for cone index (13.9%), then for tillage resistance (9.32%), sand content (3.95%), moisture content (3.34%), bulk density (2.4%), and finally clay+silt content (2.2%). The generated maps showed that the soil compaction, moisture and tillage resistance had certain orientations with respect to the field plots.

Viable bacterial population and persistence of foodborne pathogens on the pear carpoplane.

Knowledge on the culturable bacteria and foodborne pathogen presence on pears is important for understanding the impact of postharvest practices on food safety assurance. Pear fruit bacteria were investigated from the point of harvest, following chlorine drenching and after controlled atmosphere (CA) storage to assess the impact on natural bacterial populations and potential foodborne pathogens. Salmonella spp. and Listeria monocytogenes were detected on freshly harvested fruit in season one. During season one, chemical drenching and CA storage did not have a significant effect on the bacterial load of orchard pears, except for two farms where the populations were lower 'after CA storage'. During season two, bacterial populations of orchard pears from three of the four farms increased significantly following drenching; however, the bacterial load decreased 'after CA storage'. Bacteria isolated following enumeration included Enterobacteriaceae, Microbacteriaceae, Pseudomonadaceae and Bacillaceae, with richness decreasing 'after drench' and 'after CA storage'. Salmonella spp. and L. monocytogenes were not detected after postharvest practices. Postharvest practices resulted in decreased bacterial species richness. Understanding how postharvest practices have an impact on the viable bacterial populations of pear fruit will contribute to the development of crop-specific management systems for food safety assurance. © 2016 Society of Chemical Industry.

THE COMBINATION OF UAV SURVEY AND LANDSAT IMAGERY FOR MONITORING OF CROP VIGOR IN PRECISION AGRICULTURE

Abstract. Mapping of the with-in field variability of crop vigor has a long tradition with a success rate ranging from medium to high depending on the local conditions of the study. Information about the development of agronomical relevant crop parameters, such as above-ground biomass and crop nutritional status, provides high reliability for yield estimation and recommendation for variable rate application of fertilizers. The aim of this study was to utilize unmanned and satellite multispectral imaging for estimation of basic crop parameters during the growing season. The experimental part of work was carried out in 2014 at the winter wheat field with an area of 69 ha located in the South Moravia region of the Czech Republic. An UAV imaging was done in April 2014 using Sensefly eBee, which was equipped by visible and near infrared (red edge) multispectral cameras. For ground truth calibration the spectral signatures were measured on 20 sites using portable spectroradiometer ASD Handheld 2 and simultaneously plant samples were taken at BBCH 32 (April 2014) and BBCH 59 (Mai 2014) for estimation of above-ground biomass and nitrogen content. The UAV survey was later extended by selected cloud-free Landsat 8 OLI satellite imagery, downloaded from USGS web application Earth Explorer. After standard pre-processing procedures, a set of vegetation indices was calculated from remotely and ground sensed data. As the next step, a correlation analysis was computed among crop vigor parameters and vegetation indices. Both, amount of above-ground biomass and nitrogen content were highly correlated (r &gt; 0.85) with ground spectrometric measurement by ASD Handheld 2 in BBCH 32, especially for narrow band vegetation indices (e.g. Red Edge Inflection Point). UAV and Landsat broadband vegetation indices varied in range of r = 0.5 – 0.7, highest values of the correlation coefficients were obtained for crop biomass by using GNDVI. In all cases results from BBCH 59 vegetation stage showed lower relationship to vegetation indices. Total amount of aboveground biomass was identified as the most important factor influencing the values of vegetation indices. Based on the results can be assumed that UAV and satellite monitoring provide reliable information about crop parameters for site specific crop management. The main difference of their utilization is coming from their specification and technical limits. Satellite survey can be used for periodic monitoring of crops as the indicator of their spatial heterogeneity within fields, but with low resolution (30 m per pixel for OLI). On the other hand UAV represents a special campaign aimed on the mapping of high-detailed spatial inputs for site specific crop management and variable rate application of fertilizers.

THE COMBINATION OF UAV SURVEY AND LANDSAT IMAGERY FOR MONITORING OF CROP VIGOR IN PRECISION AGRICULTURE

Mapping of the with-in field variability of crop vigor has a long tradition with a success rate ranging from medium to high depending on the local conditions of the study. Information about the development of agronomical relevant crop parameters, such as above-ground biomass and crop nutritional status, provides high reliability for yield estimation and recommendation for variable rate application of fertilizers. The aim of this study was to utilize unmanned and satellite multispectral imaging for estimation of basic crop parameters during the growing season. The experimental part of work was carried out in 2014 at the winter wheat field with an area of 69 ha located in the South Moravia region of the Czech Republic. An UAV imaging was done in April 2014 using Sensefly eBee, which was equipped by visible and near infrared (red edge) multispectral cameras. For ground truth calibration the spectral signatures were measured on 20 sites using portable spectroradiometer ASD Handheld 2 and simultaneously plant samples were taken at BBCH 32 (April 2014) and BBCH 59 (Mai 2014) for estimation of above-ground biomass and nitrogen content. The UAV survey was later extended by selected cloud-free Landsat 8 OLI satellite imagery, downloaded from USGS web application Earth Explorer. After standard pre-processing procedures, a set of vegetation indices was calculated from remotely and ground sensed data. As the next step, a correlation analysis was computed among crop vigor parameters and vegetation indices. Both, amount of above-ground biomass and nitrogen content were highly correlated (r &amp;gt; 0.85) with ground spectrometric measurement by ASD Handheld 2 in BBCH 32, especially for narrow band vegetation indices (e.g. Red Edge Inflection Point). UAV and Landsat broadband vegetation indices varied in range of r = 0.5 – 0.7, highest values of the correlation coefficients were obtained for crop biomass by using GNDVI. In all cases results from BBCH 59 vegetation stage showed lower relationship to vegetation indices. Total amount of aboveground biomass was identified as the most important factor influencing the values of vegetation indices. Based on the results can be assumed that UAV and satellite monitoring provide reliable information about crop parameters for site specific crop management. The main difference of their utilization is coming from their specification and technical limits. Satellite survey can be used for periodic monitoring of crops as the indicator of their spatial heterogeneity within fields, but with low resolution (30 m per pixel for OLI). On the other hand UAV represents a special campaign aimed on the mapping of high-detailed spatial inputs for site specific crop management and variable rate application of fertilizers.

Towards Farm-Oriented Open Data in Europe: the Scope and Pilots of the European Project "FOODIE"

The different groups of stakeholders involved in the agricultural activities have to manage many different and heterogeneous sources of information that need to be combined in order to make economically and environmentally sound decisions, which include (among others) the definition of policies (subsidies, standardisation and regulation, national strategies for rural development, climate change), development of sustainable agriculture, harvest timing and yield estimation, crop damages detection, etc. The European project called “Farm-Oriented Open Data in Europe” with abbreviation FOODIE, funded between years 2014 and 2017 addresses the above mentioned issues. This paper describes the scope of the project with emphasis on its pilots. The Czech pilot is then analysed in detail including its three scenarios: Improving efficiency of transport in agriculture, Telematics of farm machinery and Monitoring of in-field variability for site specific crop management.

Towards Farm-Oriented Open Data in Europe: the Scope and Pilots of the European Project "FOODIE"

The different groups of stakeholders involved in the agricultural activities have to manage many different and heterogeneous sources of information that need to be combined in order to make economically and environmentally sound decisions, which include (among others) the definition of policies (subsidies, standardisation and regulation, national strategies for rural development, climate change), development of sustainable agriculture, harvest timing and yield estimation, crop damages detection, etc. The European project called “Farm-Oriented Open Data in Europe” with abbreviation "FOODIE", funded between years 2014 and 2017 addresses the above mentioned issues. This paper describes the scope of the project with emphasis on its pilots. The Czech pilot is then analysed in detail including its three scenarios: Improving efficiency of transport in agriculture, Telematics of farm machinery and Monitoring of in-field variability for site specific crop management.

Application of the Crop Carbon Progress Calculator in a ‘farm to ship’ cotton production case study in Australia

The various initiatives in the market place to quantify the sustainability levels of products are putting pressure on farmers to demonstrate a reduction in the environmental impacts of their crop management practices, and in particular with the lowering of the carbon footprints of their crops. At present there is no internationally accredited common method or carbon footprint model which generates site specific and LCA aligned emission estimates. The application of the Crop Carbon Progress Calculator (CCAP) is demonstrated for an irrigated cotton ‘farm to ship’ case study in Australia where we determine that the progress made in the 2011 crop against a 2002 crop base year amounts to 44% reduction in GHG emission levels.We estimate that for this particular case study the total carbon footprint of producing a bale of cotton up to ship's side or point of export is 323 kg CO2e. This includes 182 kg CO2e from the farm production phase, 73.1 kg CO2e from the gin to port supply chain, and 68.1 kg CO2e that results from emission from the stock piled gin trash at the gins. It appears that a feasible option to avoid these trash emissions is to incorporate the waste at farm level. Our analysis shows that this could generate an emissions credit of 48.8 kg CO2e per bale at farm level, which will amount to a 27% reduction in the farm emissions footprint and a 15% reduction in the whole farm to ship carbon footprint. Due to a number of site specific environmental and crop management factors, there can be significant variances in crop carbon footprint outcomes.

An analysis of factors determining spatial variable grain yield of winter wheat

We analyzed under the temperate weather conditions of northwest Germany the relationships between soil water supply, crop canopy dynamics, radiation interception, components of the soil water balance and grain yield of winter wheat using data obtained from field experiments during three years. A dynamic model of the soil water budget in combination with frequent measurements of canopy parameters thereby was used to estimate the components of the water balance and radiation interception site specifically. Differences in soil texture and soil water supply characteristics in combination with variable canopy dynamics lead to substantial differences in radiation interception, components of the soil water budget and yield. The sum of intercepted photosynthetic active radiation corrected for drought stress by the ratio of actual to potential transpiration showed a unique positive correlation to grain yield over the three experimental years. Positive correlations between grain yield and actual transpiration normalized by saturation deficit were also found, however, the slopes differed between years. The best predictor of grain yield was the ratio of the sum actual transpiration to the sum of potential evapotranspiration. We could also show that drought stress corrected radiation interception and the ratio ΣTa/ΣETp evaluated at end of May already correlated significantly with final grain yield. This may offer options for improved site specific crop management.

Chlorpyrifos-Treated Crops in the Vicinity of Surface Water Contamination in the San Joaquin Valley, California, USA

Due to frequent contamination of streams in the San Joaquin Valley, California, USA, with the insecticide chlorpyrifos, researchers are working to identify crop-specific management practices that will reduce the offsite movement of this compound into surface waters. To guide this effort, crops treated with chlorpyrifos in the vicinity of contaminated streams were identified; walnut, alfalfa, and almond were the primary crops identified. Use was higher on walnut and almond, but due to irrigation practices offsite movement in surface runoff may be more likely from alfalfa. Based on these findings, development of management practices to reduce off-site movement of chlorpyrifos in irrigation runoff from treated alfalfa fields is recommended.