Accurate estimation of tuber size distribution (TSD) parameters in discretely categorized potato (Solanum tuberosum L) yield samples is desirable for estimating modal tuber sizes, which is fundamental to yield prediction. In the current work, systematic yield digs were conducted on five commercial fields (N = 119) to compare the Weibull, Gamma and Gaussian distribution functions for relative-likelihood-based goodness-of-fit to the observed discrete distributions. Parameters were estimated using maximum likelihood estimation (MLE) for the three distributions but were also derived using the percentiles approach for the Weibull distribution to compare accuracy against the MLE approaches. The relationship between TSD and soil nutrient variability was examined using the best-fitting model's parameters. The percentiles approach had lower overall relative likelihood than the MLE approaches across five locations, but had consistently lower Root Mean Square Error in the marketable tuber size range. Negative relationships were observed between the percentile-based shape parameter and the concentrations of Phosphorus and Nitrogen, with significant (non-zero-overlapping 95% confidence interval) regression coefficients for P (−0.74 ± 0.33 for distribution of proportional tuber numbers and −1.3 ± 0.62 for tuber weights). Stem density was negatively associated with the scale and mode of tuber number (regression coefficients −0.98 ± 0.63 and −1.08 ± 0.78 respectively) and tuber weight (regression coefficients −0.99 ± 0.78 and −1.04 ± 0.69 respectively) distributions. Phosphorus is negatively related to the scale of the tuber-number-based distribution while positively associating with the tuber weight distribution. The results suggest that excess P application was associated with the increase in small tubers that did not contribute significant weight to the final yield.

Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations.

Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). Dry matter intake and IT were recorded using feed bins. For research purposes, in this work, our software sensor operated off-line. That is, the predictor variables (DMI, IT) were extracted from the recorded data (rather than from an on-line sensor). A total of 37 individual dynamic patterns of methane production were analyzed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin’s concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. As IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.

The aim of the current study was to define, optimize and customize IRRISAT, a fully operative satellite-based irrigation advisory service (IAS) provided in Campania Region, Italy, using a choice experiment to determine the preferences of farmers regarding the main characteristics and attributes of IRRISAT. Furthermore, willingness to pay for the main attributes of the services provided was estimated. The information on the amount of water required for irrigation provided by the IAS is sent out to farmers via SMS and email, as well as via the IRRISAT webpage. The study was related to the 2013–2014 irrigation season, when the service provided support to 669 farmers over an area of 55 ha. The study considered four attributes and levels of IRRISAT service: land management unit (scale of service); different levels of water saving (5, 10 and 30%) that could be achieved at different prices; annual fee paid by farmers (ranging between €6 and €10/ha/year); length of contract for the service supply, ranging from 1 to 3 years. Results showed that farmers’ preferences are influenced positively by scale (entire area of the farm instead of single fields) and duration of the service delivering contract. Concerning the duration of the contract, the most preferred option was the 3-year service. Finally, water saving was shown to affect farmers’ choices very little and thus it is probably less attractive for farmers probably due to the low price and to a relatively large availability of water for irrigation.

This study was undertaken to evaluate the effect that switching from conventional to precision feeding systems during the growing-finishing phase would have on the potential environmental impact of Brazilian pig production. Standard life-cycle assessment procedures were used, with a cradle-to-farm gate boundary. The inputs and outputs of each interface of the life cycle (production of feed ingredients, processing in the feed industry, transportation and animal rearing) were organized in a model. Grain production was independently characterized in the Central-West and South regions of Brazil, whereas the pigs were raised in the South region. Three feeding programs were applied for growing-finishing pigs: conventional phase feeding by group (CON); precision daily feeding by group (PFG) (whole herd fed the same daily adjusted diet); and precision daily feeding by individual (PFI) (diets adjusted daily to match individual nutrient requirements). Raising pigs (1 t pig BW at farm gate) in South Brazil under the CON feeding program using grain cultivated in the same region led to emissions of 1840 kg of CO2-eq, 13.1 kg of PO4-eq and 32.2 kg of SO2-eq. Simulations using grain from the Central-West region showed a greater climate change impact. Compared with the previous scenario, a 17% increase in climate change impact was found when simulating with soybeans produced in Central-West Brazil, whereas a 28% increase was observed when simulating with corn and soybeans from Central-West Brazil. Compared with the CON feeding program, the PFG and PFI programs reduced the potential environmental impact. Applying the PFG program mitigated the potential climate change impact and eutrophication by up to 4%, and acidification impact by up to 3% compared with the CON program. Making a further adjustment by feeding pigs according to their individual nutrient requirements mitigated the potential climate change impact by up to 6% and the potential eutrophication and acidification impact by up to 5% compared with the CON program. The greatest environmental gains associated with the adoption of precision feeding were observed when the diet combined soybeans from Central-West Brazil with corn produced in Southern Brazil. The results clearly show that precision feeding is an effective approach for improving the environmental sustainability of Brazilian pig production.

The objective of this study was to validate an electronic system for monitoring individual feeding and drinking behaviour and intake developed for young cattle housed in group. A total of 35 Holstein–Gyr crossbred heifers (BW: 180±52 kg; age: 121.5±32.5 days), fitted with an ear tag containing a unique passive transponder, were distributed in three groups of 12, 12 and 11 animals per period and had free access to 12 electronic feed bins and two electronic water bins (Intergado® Ltd). The dimensions of feed and water bins, as well as the sensors position were appropriate for young cattle. The system documented the visit frequency and duration, as well as the feed and water intakes, by recording the animal’s identification tag, bin number, initial and final times of visits and the difference of feed/water weight at the start and end of each bin visit. Feed bins were monitored using time-lapse video recording over 4 days and the water bins were monitored over 6 days. For each feed bin, two feeding events were monitored using manual weighings with an external scale immediately before and after the animal’s visit and the difference between them was assumed as feed intake (n=24 observations). For the water bins, 60 manual weighings were made. Video and manual weighing data were regressed on the electronic feeding and drinking behaviour and intake data to evaluate the system’s precision and accuracy. The system showed high specificity (98.98% and 98.56% for the feed and water bins, respectively) and sensitivity (99.25% and 98.74%, respectively) for identifying an animal’s presence or absence. Duration of feed and water bin visits as well as feed and water consumption per visit estimated by the system were highly correlated and precise compared with the observed video and manual weighing data (r2=0.917, 0.963, 0.973 and 0.986, respectively). It was concluded that Intergado® system is a useful tool for monitoring feeding and drinking behaviour as well as water and feed intakes in young cattle housed in groups.

Farmers are interested in knowing whether applying inputs at variable rates across a field is economically viable. The answer depends on the crop, the input, their prices, the cost of variable rate technology (VRT) versus uniform rate technology (URT), and the spatial and yield response variability within each field. Methods were investigated for determining the range of spatial variability over which the return to VRT covers its additional cost compared with URT in fields with multiple management zones. Models developed in this article, or variants thereof, could be used to help farmers make the VRT adoption decision.

Site-specific weed management recommendations require knowledge of weed species, density, and location in the field. This study compared several sampling techniques to estimate weed density and distribution in two 65-ha no-till Zea mays–Glycine max rotation fields in eastern South Dakota. The most common weeds (Setaria viridis, Setaria glauca, Cirsium arvense, Ambrosia artemisiifolia, and Polygonum pensylvanicum) were counted by species in 0.1-m2 areas on a 15- by 30-m (1,352 points in each field) or 30- by 30-m (676 points in each field) grid pattern, and points were georeferenced and data spatially analyzed. Using different sampling approaches, weed populations were estimated by resampling the original data set. The average density for each technique was calculated and compared with the average field density calculated from the all-point data. All weeds had skewed population distributions with more than 60% of sampling points lacking the specific weed, but very high densities (i.e., > 100 plants m−2) were also observed. More than 300 random samples were required to estimate densities within 20% of the all-point means about 60% of the time. Sampling requirement increased as average density decreased. The W pattern produced average species densities that often were similar to the field averages, but information on patch location was absent. Weed counts taken on the 15- by 30-m grid were dependent spatially and weed contour maps were developed. Kriged maps presented both density and location of weed patches and could be used to establish management zones. However, grid-sampling production fields on a small enough scale to obtain spatially dependent data may have limited usefulness because of time, cost, and labor constraints.

The objective of this research was to evaluate the accuracy of remote sensing for detecting weed infestation levels during early-season Glycine max production. Weed population estimates were collected from two G. max fields approximately 8 wk after planting during summer 1998. Seedling weed populations were sampled using a regular grid coordinate system on a 10- by 10-m grid. Two days later, multispectral digital images of the fields were recorded. Generally, infestations of Senna obtusifolia, Ipomoea lacunosa, and Solanum carolinense could be detected with remote sensing with at least 75% accuracy. Threshold populations of 10 or more S. obtusifolia or I. lacunosa plants m−2 were generally classified with at least 85% accuracy. Discriminant analysis functions formed for detecting weed populations in one field were at least 73% accurate in identifying S. obtusifolia and I. lacunosa infestations in independently collected data from another field. Due to highly variable soil conditions and their effects on the reflectance properties of the surrounding soil and vegetation, accurate classification of weed-free areas was generally much lower. Current remote sensing technology has potential for in-season weed detection; however, further advancements of the technology are needed to insure its use in future prescription weed management systems.

Managing weed infestations in a spatially precise manner requires accurate and cost-effective weed identification techniques. The goal of our research was to quantify the accuracy of continuous weed presence–absence maps and assess how management based on those maps may affect producer net returns. Each continuous sampled map covered the entire field and contained vector polygons labeled as either wild oat presence or wild oat absence. The accuracy of the continuous wild oat maps at each sampling time was determined from georeferenced quadrats of wild oat densities. The accuracy of the continuous wild oat seedling maps ranged from 48.3 to 87.1% among the six site-years. The accuracy of the wild oat seedling maps improved by at least 8% when a 10-m buffer was included around areas mapped as wild oat presence. The accuracy of continuous wild oat panicle maps from the combine at harvest ranged from 65.8 to 90.9% among the six site-years. The variation in accuracy for the wild oat seedling maps among sites was greater than the accuracy of the panicle maps. Net returns ($ ha−1) for four site-years were calculated and compared for four possible weed management approaches on each field. A site-specific herbicide application to areas mapped as wild oat presence always generated higher net returns than a herbicide application over the entire field for four sites. A site-specific herbicide application to areas mapped as wild oat presence plus a surrounding 10-m buffer area only resulted in a higher net return in one of the 12 site-years compared with a site-specific herbicide application without the 10-m buffer. This site had the lowest (48.3%) wild oat seedling map accuracy, and uncontrolled wild oat had a high-yield effect. This research indicates that using a continuous weed sampling method based on presence or absence for site-specific herbicide application can be profitable over a herbicide application to the entire field, even with the associated technology cost and seedling map errors.

One of the primary benefits of site-specific agricultural technologies is the potential to reduce the use of polluting inputs, thereby minimizing ecological damage. Weeds are often found in patches, so site-specific (field scale) management offers a straightforward opportunity to minimize ecological effects related to wasteful broadcast use of herbicides. Beyond possible efficiencies related to accurate targeting, site-specific technologies, through a process of parameterizing management decision models for each field, may improve ecological understanding of weed populations and thus encourage ecologically based management. This hypothesis was assessed with a simple model that combined economic injury–level prediction with a single parameter (growing season precipitation) to represent environmental variability. Model simulations of crop yield in response to weed density at a virtual farm and six surrounding regional experiment stations suggested that localized (on-farm field) parameter estimation may help to circumvent the variability associated with damage function extrapolation from small-plot experiments at experiment stations and thereby improve predictive accuracy for site-specific weed management (SSWM) strategies. Thus, remote sensing and SSWM technologies may allow producers to reduce the risk associated with the reduced use of purchased inputs and greater reliance on natural weed population–regulating mechanisms. Effective ecologically based weed management may be dependent on local parameterization of models.

Geostatistical techniques were used to describe and map the spatial distribution of crenate broomrape populations parasitizing broad bean over 6 yr (from 1985 to 1990). In the first year, the spatial distribution was random, but from 1986 to 1989, crenate broomrape populations were clearly aggregated. The crenate broomrape infection severity (IS: number of emerged broomrape m−2) increased every year, from an average of 0.45 in 1985 to 29.4 in 1989, with a slight decrease the following year (IS = 27.4). Spherical functions provided the best fit because the cross-validation criteria were accomplished in all study cases. Kriged estimates were used to draw contour maps of the populations. About 34.3, 43.3, and 74.3% of the field plot surface exhibited an IS ≥ 1 (economic threshold) in 1985, 1986, and 1987, respectively, and nearly 100% of the area exceeded the economic threshold from 1988 to 1990; 1985 and 1986 were key years for control of the parasitic weed population. The percentage of infested area at different IS intervals in each year's map obtained by kriging was used to estimate the percentage of yield losses in each infested area (YA) with the equation: YA = A ∗ Ymax ∗ (1 − IS ∗ 0.124), where A is the infested area at a given IS interval and Ymax is the expected broomrape-free broad bean yield. Yield losses under different IS intervals were compared with yield loss attributable to a uniform distribution of crenate broomrape. Results showed that yield loss assuming a uniform distribution of crenate broomrape was clearly overestimated, which is important to avoid overuse of herbicides.

Weed population estimates were collected from four Glycine max fields during the summers of 1997 and 1998. Seedling weed populations were sampled using a regular coordinate system on a grid either 50 by 50, 30 by 30, or 10 by 10 m. MSU-HERB and Mississippi Herbicide Application Decision Support System (HADSS) (yield loss prediction and herbicide recommendation models for G. max) were used to determine the estimated net gain resulting from simulated herbicide applications at each sample location in each field. When necessary, the appropriate data points from the 10- by 10-m grid were removed to form population data sets on grids 20 by 20, 40 by 40, and 80 by 80 m. The objectives of this research were to compare estimated economic returns of site-specific herbicide management and broadcast herbicide management in nontransgenic and glyphosate-tolerant G. max and to evaluate the effects of various weed sampling intensities on estimated economic returns from site-specific herbicide applications. Site-specific herbicide management was the compilation of simulated herbicide treatments giving the highest estimated net gains at each location within each field. Broadcast herbicide management was the simulated broadcast application giving the highest estimated net gain for each field. Sampling costs and the unattainable site-specific application costs were not included in the estimated net gain calculations. In nontransgenic G. max production, the estimated net gain for treating the four fields with site-specific technology was $104.76 ha−1 higher than when using the optimum broadcast herbicide. In glyphosate-tolerant G. max production, the average estimated net gain for site-specific treatment of the fields was $96.24 ha−1 higher than for treatment with the best broadcast herbicide application. In nontransgenic G. max, the estimated net gain resulting from site-specific applications on a 10-m grid was $77.17 ha−1 higher than from site-specific applications on a 20-m grid; however, in glyphosate-tolerant G. max, this difference was only $19.84 ha−1. Increased estimated net gain resulted primarily from the use of herbicides that maximized return for each field area and from the decrease of unnecessary herbicide applications because of below-threshold weed infestations.

The size, location, and variation in time of weed patches within an arable field were analyzed with the ultimate goal of simplifying weed mapping. Annual and perennial weeds were sampled yearly from 1993 to 1997 at 410 permanent grid points in a 1.3-ha no-till field sown to row crops each year. Geostatistical techniques were used to examine the data as follows: (1) spatial structure within years; (2) relationships of spatial structure to literature-derived population parameters, such as seed production and seed longevity; and (3) stability of weed patches across years. Within years, densities were more variable across crop rows and patches were elongated along rows. Aggregation of seedlings into patches was strongest for annuals and, more generally, for species whose seeds were dispersed by combine harvesting. Patches were most persistent for perennials and, more generally, for species whose seeds dispersed prior to expected dates of combine harvesting. For the most abundant weed in the field, the annual, Setaria viridis, locations of patches in the current year could be used to predict patch locations in the following year, but not thereafter.

The objective of this research was to assess the accuracy of remote sensing for detecting weed species in soybean based on two primary criteria: the presence or absence of weeds and the identification of individual weed species. Treatments included weeds (giant foxtail and velvetleaf) grown in monoculture or interseeded with soybean, bare ground, and weed-free soybean. Aerial multispectral digital images were collected at or near soybean canopy closure from two field sites in 2001. Weedy pixels (1.3 m2) were separated from weed-free soybean and bare ground with no more than 11% error, depending on the site. However, the classification of weed species varied between sites. At one site, velvetleaf and giant foxtail were classified with no more than 17% error, when monoculture and interseeded plots were combined. However, classification errors were as high as 39% for velvetleaf and 17% for giant foxtail at the other site. Our results support the idea that remote sensing has potential for weed detection in soybean, particularly when weed management systems do not require differentiation among weed species. Additional research is needed to characterize the effect of weed density or cover and crop–weed phenology on classification accuracies.

Targeting weed patches for site-specific herbicide applications potentially represents cost savings for operators, reduction in environmental herbicide effects, and increased efficiency of weed control. An experiment was initiated in a no-till corn field in Ontario, Canada, in 1998 and was continued in rotation with no-till soybeans in 1999. Weeds were intensively scouted, and distribution maps of the most common weeds (field horsetail, spiny sowthistle, dandelion, and common lambsquarters) were generated for both years. A prescription map for each plot was made using the weed density maps. Treatment decisions were based on a weed threshold value of 1 shoot m−2. Four herbicide treatments were compared: a conventional broadcast, a site-specific application targeting weed patches only, and two combinations of broadcast and site-specific applications. Treatments were applied using a direct-injection sprayer. Efficacy of weed control and yield were compared among treatments. In 1998 and 1999 there were no differences in the level of weed control or yield among treatments. The average percent area sprayed was reduced as much as 26% in the site-specific treatment in 1998 and up to 59% in the site-specific and broadcast combination treatments in 1999. For those species present in the field, patches ranged from highly aggregated to completely random, and patch stability ranged from very stable to very unstable over the 2 yr.

The adoption of precision technologies that spatially register measurements using global positioning systems (GPS) greatly facilitates conducting large-scale on-farm research by farmers. On-farm experiments that utilize producer equipment include variations in agronomic practices that occur in situations where we want to predict the effect of inputs on yield. The domain of inference for such on-farm studies therefore more closely matches that desired by researchers. To investigate the feasibility of on-farm research using GPS, a study was conducted to evaluate the potential benefit of site-specific weed management. The study utilized producer-maintained field-scale equipment on four Montana farms in dryland spring wheat production. Paired site-specific and whole-field herbicide treatment areas were established in 0.9 to 1.9-ha blocks using consultant weed maps and a geographic information system (GIS). Yield was unaffected by herbicide treatment strategy (site-specific or broadcast). Minimal detectable yield differences were evaluated for the experimental design (0.2 T ha−1). Net returns increased when the percentage of field infested by wild oat decreased. Visual ratings of wild oat density taken at harvest indicated no difference in wild oat control between treatments in two of four site-years. This research suggests that producer-owned equipment can be used to compare treatments, but the accuracy and subsequent power of such comparisons are likely to be low.

Initial ideas on risk management uses of precision agricultural technology focused on site-specific treatment of problem areas to reduce the probability of low yields and returns. Recent discussions deal with sensor and remote-sensing information to improve marketing and “as applied maps” as trace-back mechanisms to manage liability. A theoretical model is presented that suggests that there are plausible circumstances under which precision farming can reduce temporal yield variability. Empirical evidence from an on-farm trial of site-specific P&K management in the Eastern Cornbelt supports the hypothesis that precision farming can have risk-reducing benefits.

Probit analysis identified factors that influence the adoption of precision farming technologies by Southeastern cotton farmers. Younger, more educated farmers who operated larger farms and were optimistic about the future of precision farming were most likely to adopt site-specific information technology. The probability of adopting variable-rate input application technology was higher for younger farmers who operated larger farms, owned more of the land they farmed, were more informed about the costs and benefits of precision farming, and were optimistic about the future of precision farming. Computer use was not important, possibly because custom hiring shifts the burden of computer use to agribusiness firms.

Potential benefits of variable rate nitrogen application are illustrated and information needs identified. Lower costs of precision farming services, higher crop prices, and greater divergence in yield response potentials across management zones reduce the spatial variability required for profitable variable rate application. Information needs include identification and measurement of management zones within a field and estimation of management zone yield response functions, crop and input prices, and the cost of precision farming services.

Meta-response functions for corn yields and nitrogen losses were estimated from EPIC-generated data for three soil types and three weather scenarios. These metamodels were used to evaluate variable rate (VRT) versus uniform rate (URT) nitrogen application technologies for alternative weather scenarios and policy options. Except under very dry conditions, returns per acre for VRT were higher than for URT and the economic advantage of VRT increased as realized rainfall decreased from expected average rainfall. Nitrogen losses to the environment from VRT were lower for all situations examined, except on fields with little spatial variability.