Crop Performance Data Research Articles

Precision of grain yield monitors for use in on-farm research strip trials

On-farm research (OFR) has become popular as a result of precision agriculture technology simplifying the process and farm software capabilities to summarize results collected through the technology. Different OFR designs exists with strip-trials being a simple approach to evaluate different treatments. Common in OFR is the use of yield monitors to collect crop performance data since yield represents a primary response variable in these type studies. The objective was to investigate the ability of grain yield monitoring technologies to accurately inform strip trials when frequent yield variability exists within an experimental unit. A combination of six sub-plot treatment resolutions (TR) that differed in length of imposed yield variation (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) were harvested at combine ground speeds of 3.2, 6.4, 7.2, and 8.1 kph, depending on study site (three study sites total). Intentional yield differences in maize (Zea mays L.) were created for each sub-plot by alternating the amount nitrogen (N) applied: 0 or 202 kg N/ha. Yield was measured by four commercially available yield monitoring (YM) technologies and a weigh wagon. Comparisons were made between the accumulated mass of the YM technology and weigh wagon through percent differences along with testing the significance of the plotted relationship between YM and weigh wagon. Results indicated that yield monitoring technology can be used to evaluate strip trial performance regardless of yield frequency and variability (error < 3%) within an experimental unit when operating within the calibrated range of the mass flow sensor. Operating outside of the calibrated range of the mass flow sensor resulted in > 15% error in estimating accumulated weight and overestimation of yield by 23%. Finally, no significant differences existed in estimating accumulated weight values between grain yield monitor technologies (all p-values ≥ 0.54).

Smart Farming Techniques for Climate Change Adaptation in Cyprus

Smart farming based on Internet of Things (IoT) technologies enables crop farmers to collect real-time data related to irrigation and plant protection processes, aiming to increase production volume, improve product quality, and predict diseases, while optimizing resources and farming processes. IoT devices can collect vast amounts of environmental, soil, and crop performance data, thus building time series data that can be analyzed to forecast and compute recommendations and deliver critical information to farmers in real time. In this sense, the added-value from the farmers’ perspective is that such smart farming techniques have the potential to deliver a more sustainable agricultural production, based on a more precise and resource-efficient approach in the complex and versatile agricultural environment. The aim of this study is to investigate possible advantages of applying the Smart Farming as a Service (SFaaS) paradigm, aiming to support small-scale farmers, by taking over the technological investment burden and offering next generation farming advice through the combined utilization of heterogeneous information sources. The overall results of the pilot application demonstrate a potential reduction of up to 22% on total irrigation needs and important optimization opportunities on pesticides use efficiency. The current work offers opportunities for innovation targeting and climate change adaptation options (new agricultural technologies), and could help farmers to reduce their ecological footprint.

Development of an Internet of Things(IoT) Monitoring Framework for Assisted Agriculture

Internet of Things (IoT) is the cutting edge technology which is going to create a lot of opportunities in the near future. Development of Smart Devices that interact with the environment and make intelligent decisions will make life easier for all. Improving agricultural productivity is essential for increasing profitability and meeting the rapidly growing demand for agricultural produce due to rapid population growth across the world. Agricultural productivity can be increased by understanding and forecasting crop performance which is presently done manually in our country. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). Additionally, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators. All this also consumes a lot of time and energy when done manually. Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. This data can then be analyzed to filter out invalid data and compute personalized crop recommendations for any specific agricultural land. In this paper, we propose to design and develop an IoT based Monitoring, Controlling and Decision Support framework that can automate the collection of environmental, soil, fertilization, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalized crop recommendations for any agricultural farm.

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt.

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

DUDE: A User‐Friendly Crop Information System

A crop information system facilitates the storage, retrieval, and utilization of historical crop performance data. Here, we report a user‐friendly crop information system entitled Data Unification and Distillation Engine (DUDE), which (i) converts data from spreadsheet formats to a relational database, (ii) allows editing of tables in the database, (iii) simplifies the construction and execution of queries from the database, (iv) formats queried data into a variety of file structures for data analyses, and (v) simplifies maintenance and corrections of data in the database. DUDE runs on any personal computer that operates with Windows 2000 or later versions of Microsoft Windows. DUDE is freely available from the authors.

Information systems for crop performance data

An increased need for efficient storage and retrieval of crop performance data is driven by a desire to increase the value of crop performance tests, opportunities in crop modelling, opportunities to facilitate cross planning, opportunities to discover genes that affect economic traits, and data mining applications that require better integration of data from multiple sources. Thus, an increased number of stakeholders need access to crop data that are current, accurate, and complete. There is also a growing sophistication and awareness of the role and capabilities of modern informatics techniques in biological research – an area that has become known as “bioinformatics”. Bioinformatics, in partnership with statistics, can play a vital role in increasing the value of crop performance data. However, much of this role remains to be developed and adopted by the communities that gather and use these data. Part of the challenge is that phenotypic data are complex, and extensive information about the context under which the data were collected is required. This can include information about experimental design, soil and climatic conditions, treatments applied, germplasm tested, plant growth stages, and traits measured. If context is neglected, data are useless, but if context is overly complex, it may be ignored or used improperly. Several solutions have been developed to address these needs. These include commercial software packages, open-source collaborations, and a new application developed by the authors. Each solution has strengths and weaknesses, and each addresses different types of needs. This review will discuss the motivations for developing and using crop information systems, the current status and availability of crop information systems, and the challenges that must be met to achieve future potential. Key words: Bioinformatics, database, software, statistics, ontology, variety trial

Repeatability of Model Genetic Coefficients Derived from Soybean Performance Trials across Different States

Crop model testing in diverse environments is essential if modelers wish to make applications or extrapolations to those environments. A recent study demonstrated the effectiveness of optimization techniques for deriving cultivar coefficients for the CROPGRO-Soybean model from typical information provided by soybean performance tests. The objectives of this study were (i) to explore the extent to which cultivar coefficients developed by these approaches from crop performance tests are stable across different regions, (ii) to test the CROPGRO-Soybean model's ability to predict phenology and seed yield using cultivar coefficients that were developed in different regions, and (iii) to investigate whether 3 yr of crop performance data are adequate for developing stable genetic coefficients. A stepwise procedure was applied to derive cultivar coefficients for 10 common cultivars grown in different environments in Georgia and North Carolina. Regarding the transportability of cultivar coefficients across states, we found that the critical daylength coefficients were the most reliable cultivar traits. We found less stability of the cultivar traits that control genetic differences in seed yield potential. The estimated cultivar coefficients developed in Georgia enabled CROPGRO to predict yield and harvest maturity in North Carolina within 3.8% and 3.5 d, respectively, from the observed averages. Using the cultivar coefficients developed from North Carolina environments allowed us to simulate the actual mean yield and harvest maturity in Georgia to within 2.5% and 2.0 d. Furthermore, the model's ability to predict seed yield and maturity with cultivar coefficients developed from 3 yr of data was nearly as good as that derived from much larger data sets.

Repeatability of Model Genetic Coefficients Derived from Soybean Performance Trials across Different States.

Crop model testing in diverse environments is essential if modelers wish to make applications or extrapolations to those environments. A recent study demonstrated the effectiveness of optimization techniques for deriving cultivar coefficients for the CROPGRO-Soybean model from typical information provided by soybean performance tests. The objectives of this study were (i) to explore the extent to which cultivar coefficients developed by these approaches from crop performance tests are stable across different regions, (ii) to test the CROPGRO-Soybean model's ability to predict phenology and seed yield using cultivar coefficients that were developed in different regions, and (iii) to investigate whether 3 yr of crop performance data are adequate for developing stable genetic coefficients. A stepwise procedure was applied to derive cultivar coefficients for 10 common cultivars grown in different environments in Georgia and North Carolina. Regarding the transportability of cultivar coefficients across states, we found that the critical daylength coefficients were the most reliable cultivar traits. We found less stability of the cultivar traits that control genetic differences in seed yield potential. The estimated cultivar coefficients developed in Georgia enabled CROPGRO to predict yield and harvest maturity in North Carolina within 3.8% and 3.5 d, respectively, from the observed averages. Using the cultivar coefficients developed from North Carolina environments allowed us to simulate the actual mean yield and harvest maturity in Georgia to within 2.5% and 2.0 d. Furthermore, the model's ability to predict seed yield and maturity with cultivar coefficients developed from 3 yr of data was nearly as good as that derived from much larger data sets.

Simulating winter wheat yields under temperate conditions: exploring different management scenarios

This paper describes a methodology for analysing management strategies to find best agronomic practices using a crop simulation model (CERES-Wheat). The study area is the estate of Imperial College at Wye, in the Stour Catchment, Kent, UK, an area highly suited to winter wheat production. The model is validated using historic crop performance data. Yield responses to differing sowing rates (range 200–450 seeds m −2), sowing dates and rates of nitrogen application (between 100 and 220 kg ha −1) with soil types of medium to heavy texture were simulated under water-limited conditions using historical daily weather data. In model validation, observed yields ranged between 6.9 and 7.4 t ha −1, while simulated ranges were between 6.9 and 7.8 ha −1. The RSMD of the difference was small (0.24 t ha −1) and non-significant. Optimum management practices (in terms of planting date, seed density and nitrogen application) were thereby defined. Also, simulations of potential yield (i.e. yield with no water and nutrient stress) were run for comparison. Results of this study reveal that the calibrated and validated CERES-Wheat model can be successfully used for the prediction of wheat growth and yield under conditions appropriate to Western Europe.

Using Crop Performance Data to Select Hybrids and Varieties

The proliferation of hybrids and varieties available to growers and the availability of data in various formats make varietal selection difficult. This report seeks to guide growers, consultants, and extension agents on how to use the most appropriate data sets from university trials in varietal selection. Six crops (barley [Hordeum vulgare L.], corn [Zeu mays L], cotton [Gossypium hirsutum L], oats [Avena sativa L.], soybean [Glycine max (L.) Merr.], and wheat [Triticum aestivum L.]) in the North Carolina Official Variety Trials were examined for turnover rate, variety × environmental interactions, and probabilities of predicting the top varieties in a subsequent year based on single and 2-yr data. Single-year turnover rates ranged from 27 to 51%. There were no variety × location interactions but there were variety × year interactions in barley and early and medium maturing cotton, and variety × location × year interactions in barley, MGVI soybean, and wheat. Based on all data examined in this study, single-year multi-location data would be appropriate to use in selecting midseason corn hybrids. Two-year multi-location data would be useful for barley, early and full-season corn, early and medium cotton, oats, MG V and VI soybean, and wheat. The protocol used in this study could be applied to other crops in other states recognizing that not all crops are tested equally and best data sets may be different for the various crops. Research Question University-run state crop performance trials provide unbiased information for growers, consultants, extension agents, and seed company representatives to use in making varietal selection decisions. The trials include many entries from several different companies or public institutions and are located at several sites in each state. This vast data set serves to confuse the grower and agricultural professional; they have to know the most helpful data to use in their varietal selection process. Literature Summary In Missouri and North Dakota, 2-yr multi-location means were found to be best for choosing corn hybrids. Multi-location data were the best predictors of future performance of soybean varieties in Minnesota. Private seed companies are advising that crop performance data be explained and presented in a manner that aids in interpreting the data. Study Description Six crops from the North Carolina Official Variety Trials were examined for turnover rate, significant interactions, and predictability. Ten years of data were available for barley, corn, oats, soybean, and wheat while 8 yr of data were available for cotton. The percentage of new varieties or turnover rate was determined for each year and every 2 yr. The probability of choosing high-yielding varieties or the top variety was determined from three different data sets. Applied Questions How often are new varieties entered in the crop performance trials and what are the ramifications? The turnover rate ranged from 51% of the early-season corn hybrids to only 27% of the barley varieties each year. Every 2 yr, 71% of the corn hybrids are new and only 40% of the barley varieties are new. Therefore for crops like the early-season corn hybrids, choosing hybrids from 3- and 4-yr data sets would mean some of the newer and possibly higher-yielding corn hybrids on the market would be ignored. What interactions are important and what impact will they have on interpreting that data? There were no variety × location interactions for any of the six crops, suggesting that multi-location data can be used. Variety × year interactions occurred with barley and cotton, indicating a need to use a minimum of 2-yr data. The three-way interaction (variety × location × year) was important for barley, MG VI soybean, and wheat; this would suggest the use of multi-location multi-year data. For what crops can we use single-year multi-location data to choose varieties or hybrids? These data proved adequate for mid-season corn hybrids. Which crops require multi-location 2-yr data for the best predictions and how predictive are they? Barley, early- and full-season corn hybrids, cotton, oats, MG V and VI soybean, and wheat all require 2-yr multi-location data. Predictions ran from 0.54 for medium-maturing cotton varieties to 0.94 for full-season corn hybrids for choosing the high-yielding entries. Recommendation The conclusions reached in this research pertain to North Carolina, but the principles are applicable to other states and regions. This approach should be followed whenever crop performance data are published so that growers and agricultural professionals can more efficiently select hybrids and varieties for their particular operation.

Measures of Validity in Cultivar Performance Trials

AbstractThe coefficient of variation (CV) has been used for many years by researchers to determine the validity of performance trials. The premise behind the CV, that the standard deviation is proportional to the mean, compromises one of the assumptions of a normal distribution (i.e., that the sample mean and sample variance are independent). If there is a nonnormal distribution, then the data may need to be transformed, which in turn may invalidate the use of the CV. A constant CV across trials implies a relationship between the error variance and the mean such that the slope = 2.0 in the regression of In error variance on ln mean. The purpose of this paper is to stress the relationship between the error variance and mean in the CV, review this relationship in actual yield data, and examine the effects of various transformations on CVs and coefficient of determination (R2). The ln error variance regressed on In mean for several agronomic crops ranged from −0.11 for full‐season corn (Zea mays L.) to 1.31 for oat (Avena sativa L.). Although some crops had a nonzero regression coefficient for this relationship, none approached 2.0, the level that supports the hypothesis that the CV is a viable tool for comparing the relative variation of different trials. Data transformations (e.g., square root, logarithmic, angular, inverse, reverse, and addition of a constant) tend to lower CV values in most cases, but can cause dramatic increases, depending on the nature of the variance and the specific transformation. On the other hand, R2, which is a measure of the amount of variability accounted for in the model, remains relatively unaffected by most transformations. Reasonable R2 values for determining validity of performance trials may vary by location and crop species. Examination of North Carolina data revealed that discarded trials generally had R2 values less than 50%. The R2 may be affected by the size of the dataset, and so adjusted R2 maybe more useful for comparing trials of varying sizes; however, adjusted R2 will tend to be larger where there are larger differences among entries. There is no one perfect measure of the validity of trial data, but the R2 and the adjusted R2 are reasonable alternatives to the CV and should be examined along with other statistical measures when evaluating crop performance data.

Establishing a Rejection Procedure for Crop Performance Data

AbstractCrop performance trials are conducted to provide unbiased relative performance data for breeders, growers, and extension personnel. This report presents a method for establishing a criterion level of precision for rejection of questionable data. For a given crop, historical data (a minimum of 30 data points) are used to calculate average error variance and to determine any relationship between productivity level and error variance by regressing log(variance) on log(mean) for the variable of primary interest, usually yield. A nonsignificant relationship dictates using the pooled error variance over all trials as the reference or expected variance; a significant relationship dictates using the fitted variance predicted from the regression at the observed mean yield of each trial. An upper bound on error variance (a multiple k of the expected error variance) is calculated. Any trial with an observed error variance above the upper bound would be discarded as having unusually low precision. Historical data from five crop species tested in North Carolina were examined as working examples. Of eight data sets, four showed no relationship between variance and productivity level on a log‐log scale. Compared with corn and soybean, more small‐grain (barley, oat, and wheat) trials were above the criterion level when k = 2. Thus, k‐values may need to be adjusted among crop species. Most trials with unacceptably low precision showed a record of some sort of problem, although it could not be confirmed that the noted problem caused the high variability. Researchers using the proposed procedure are cautioned to examine their data closely to ensure an appropriate k‐value and to note test sites of low precision or methods causing low precision.

1030 COMPUTERIZED MULTIFERTILIZER INJECTION SYSTEMS

Horticultural crop management strategists of the past have routinely chosen to remove root-related limitations to productivity by operating at high fertility levels and by excessive irrigation of well-drained soils, without much concern for fertilizer losses to the environment. The relatively low cost of fertilizer as a component of the overall production cost has justified, at least on economic tens. the liberal application of fertilizer on most horticultural crops. However, inefficiencies in fertilizer use can no longer be neglected as society becomes increasingly aware and critical of the valuable energy expended in fertilizer manufacturing and of the damage to the environment from runoff fertilizer. Progress made towards fertilizer use economy will be discussed in relation to the development and use of computer controlled fertilizer injectors which allow the timely application of precise amounts of nutrients according to the changing needs of crops. Seasonal fertigation programs for greenhouse vegetable production in soil and soilless media will be described and corresponding crop performance data will be presented.