USDA National Agricultural Statistics Service Research Articles

Crop sequence boundaries using USDA national agricultural statistics service historic cropland data layers

Gridded landcover datasets like the NASS Cropland Data Layer (CDL) provide a useful resource for analyses of cropland management. However, many farm operation decisions are made at the field level, not the pixel level. To capture relationships between land cover and field characteristics – size, contiguity, etc. – some method is needed to aggregate gridded data into crop fields. To provide a uniform and consistent approach for aggregation of gridded data at the field level over a series of years, this research project developed a set of Crop Sequence Boundaries (CSBs), which are polygons that delineate areas of homogeneous cropping sequences for the contiguous US. The CSBs are open-sourced algorithm-based, geospatial polygons derived using historic CDLs together with road and rail networks to capture areas with common cropping sequences. The CSB approach used geospatial functions in Google Earth Engine (GEE) and in the ArcGIS Pro application. These geospatial functions are run in parallel by sub-dividing the contiguous US into smaller regions based on road and rail boundaries to prevent overlaps or gaps in the data. As a new set of algorithmically delineated field polygons, the CSBs enhance applications requiring large-scale crop mapping with vector-based data.

Comprehensive Economic Impacts of Wild Pigs on Producers of Six Crops in the South-Eastern US and California

Wild pigs (Sus scrofa) cause damage to agricultural crops in their native range as well as in the portions of the globe where they have been introduced. In the US, states with the highest introduced wild pig populations are Alabama, Arkansas, California, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, South Carolina, and Texas. The present study summarizes the first survey-based effort to value the full extent of wild pig damage to producers of six crops in these eleven US states. The survey was distributed by the USDA National Agricultural Statistical Service in the summer of 2022 to a sample of 11,495 producers of corn (Zea mays), soybeans (Glycine max), wheat (Triticum spp.), rice (Oryza sativa), peanuts (Arachis hypogaea), and sorghum (Sorghum bicolor) in these 11 states. Our findings suggest that the economic burden of wild pigs on producers of these crops is substantial and not limited to the direct and most identifiable categories of crop damage (i.e., production value lost due to depredation, trampling and rooting). We estimate that the annual cost to producers of these six crops in the surveyed states in 2021 was almost USD 700 million.

First Report of Cytospora azerbaijanica Causing Cytospora Canker and Shoot Dieback on Peach (Prunus persica) in California, U.S.A.

Peaches (Prunus persica L.) are an important crop in the United States with California leading the nation in peach production, with approximately 505,000 tons valued at $378.3 million (USDA National Agricultural Statistics Service, 2021, https://www.nass.usda.gov/). From April to July 2022, symptoms of branch and scaffold canker as well as shoot dieback were observed in three peach (cvs. Loadel, Late Ross and Starn) orchards located in San Joaquin County, California. Samples were collected from about 12 trees for each cultivar. Fast-growing, white, flat colonies were consistently isolated from active cankers on acidified potato dextrose agar (APDA) following the method described by (Lawrence et al. 2017). Pure fungal cultures were obtained by transferring single hyphal tips onto new APDA Petri plates. A total of 22 isolates were obtained. Each fungal isolate was recovered from a single diseased branch (40 to 55% recovery). All isolates in this study shared similar morphological characteristics. Fungal colonies were fast-growing with relatively even but slightly dentate margin, flat with white to off-white mycelium that turned vinaceous buff to pale greyish sepia (Rayner 1970) with age. Black, globose, ostiolated pycnidia, 0.8-(1.3)-2.2 mm diameter, with brownish surface hyphae formed on peach wood embedded in PDA after approximately three weeks and exudated buff-colored mucilage. Pycnidia were both solitary and aggregated and had multiple internal locules sharing invaginated walls. Conidiogenous cells were hyaline, smooth-walled, septate, tapering towards the apex, 13-(18.2)-25.1 × 0.8-(1.3)-1.9 µm (n = 40). Conidia were hyaline, allantoid, smooth, aseptate, 5.5-(6.3)-7.1 × 1.4-(1.9)-2.3 µm (n = 40). Genomic DNA was extracted and sequences of the internal transcribed spacer region (ITS) using ITS5/ITS4 universal primers, translation elongation factor 1α gene (TEF) using primers EF1-728F/EF1-986R, second largest subunit of RNA polymerase II (RPB2) using primers RPB2-5F2/fRPB2-7cR, and actin gene region (ACT) using primers ACT-512F/ACT-783R were obtained and compared with sequences available in GenBank (Lawrence et al. 2018; Hanifeh et al. 2022). Isolates were identified as Cytospora azerbaijanica following DNA sequencing and morphological identification. Consensus sequences of the four genes of two representative isolates (SJC-66 and SJC-69) were deposited into GenBank database (ITS: OQ060581 and OQ060582; ACT: OQ082292, OQ082295; TEF: OQ082290 and OQ082293; RPB2: OQ082291 and OQ082294). The Basic Local Alignment Search Tool (BLAST) indicated that the sequenced RPB2 genes of isolates (SJC-66 and SJC-69) were at least 99% identical to that of Cytospora sp. strain shd47 (Accession: MW824360) covering at least 85% of the sequences. The actin genes from our isolates were at least 97.85% identical to that of Cytospora sp. strain shd47 (Accession: MZ014513), covering 100% of the sequences. The translation elongation factor gene from isolates (SJC-66 and SJC-69) was at least 96.4% identical to that of Cytospora sp. strain shd166 (Accession: OM372512), covering 100% of the query. Those top hit strains belong to C. azerbaijanica, recently reported by Hanifeh et al. (2022). Pathogenicity tests were performed by inoculating eight wounded, 2- to 3-year-old healthy branches on each of eight 7-year-old peach trees, cvs. Loadel, Late Ross and Starn, using 5-mm-diameter mycelium plugs collected from the margin of an actively growing fungal colony on APDA. Controls were mock-inoculated with sterile agar plugs. Inoculation sites were covered with petroleum jelly and wrapped with Parafilm to keep moisture. The experiment was performed twice. After four months, inoculation tests resulted in vascular discoloration (canker) above and below the inoculation sites (average necrosis length of 114.1 mm). Cytospora azerbaijanica was re-isolated from all infected branches (70 to 100% recovery) completing Koch's postulates. Controls remained symptomless and no fungi were isolated from the slightly discolored tissue. Cytospora species are destructive canker and dieback pathogens of numerous woody hosts worldwide. Recently, C. azerbaijanica was reported in causing canker disease of apple trees in Iran (Hanifeh et al. 2022). To our knowledge, this is the first report of C. azerbaijanica causing canker and shoot dieback of peach trees in the United States and worldwide. These findings will aid towards a better understanding of genetic diversity and host range of C. azerbaijanica.

Technology Use Growing on Nebraska Farms and Ranches

On Aug. 17, 2023, the USDA National Agricultural Statistics Service (NASS) released its “Technology Use (Farm Computer Usage and Ownership)” survey. According to the survey, 77% of Nebraska farmers and ranchers own or use desktop or laptop computers. That is up from 74% in 2021. Similarly, 85% own or use a smartphone, which is up from 80% in 2021. Farmers and ranchers in Nebraska are above the U.S. average in computer use as 69% of producers in the U.S. own or use a personal computer and 82% own or use a smartphone. Interestingly, 90% of farms in Nebraska reported they had internet access, just over the 85% of farms throughout the U.S. How many of these electronic devices are used for conducting farm or ranch business? Nearly 40% of respondents stated they purchased agricultural inputs over the internet (up from 31% in 2021), and 31% stated they conducted agricultural marketing activities over the internet. Approximately 25% accessed USDA/NASS reports or other USDA services over the internet, while 27% conducted business with any USDA website. This compares with 62% of respondents that conduct business with any non-agricultural website.

Implied climate warming contributions of enteric methane emissions are dependent on the estimate source and accounting methodology

The objective of this project was to dem- onstrate differences in methane (CH4) emission estimates between 2 reporting entities and to illustrate how the con- tribution to climate warming of enteric CH4 emissions is dependent on accounting methodologies. United States enteric CH4 emissions were accessed from the Food and Agriculture Organization (FAO) and US Environmental Protection Agency (EPA), and cattle inventory numbers were ob- tained from the USDA-National Agricultural Statistics Service (NASS) website and EPA spreadsheet. Enteric CH4 emission estimates from both sources were then ex- pressed as CO2 equivalence (CO2-e) and warming equiva- lence (CO2-we) using both the 100-yr global warming potential (GWP100) and the newer global warming po- tential* (GWP*) methodologies. Almost all of the year-to- year variability in the FAO data set was explained by NASS cow and calves inventory (R2 = 0.99), whereas much less variability was explained by EPA cattle inven- tory for the EPA data set (R2 = 0.45). The EPA estimates were consistently greater than FAO estimates, and only a small amount of variation was accounted for (R2 = 0.41). Despite these differences, GWP* methodologies produced much smaller CO2-e values compared with GWP100 for both data sets. This work highlight- ed several important concepts to understand. First, it is important to understand the methodology for estimating enteric CH4 emissions used by different reporting bodies. Second, with both the FAO and EPA data sources, GWP* methodology provides a smaller estimate of warming con- tribution than the GWP100 method. Finally, this evalu- ation also highlights how this greenhouse gas accounting method is not a panacea for this source of greenhouse gas emissions.

Using Small Area Estimation to Produce Official Statistics

The USDA National Agricultural Statistics Service (NASS) and other federal statistical agencies have used probability-based surveys as the foundation for official statistics for over half a century. Non-survey data that can be used to improve the accuracy and precision of estimates such as administrative, remotely sensed, and retail data have become increasingly available. Both frequentist and Bayesian models are used to combine survey and non-survey data in a principled manner. NASS has recently adopted Bayesian subarea models for three of its national programs: farm labor, crop county estimates, and cash rent county estimates. Each program provides valuable estimates at multiple scales of geography. For each program, technical challenges had to be met and a strenuous review completed before models could be adopted as the foundation for official statistics. Moving models out of the research phase into production required major changes in the production process and a cultural shift. With the implemented models, NASS now has measures of uncertainty, transparency, and reproducibility of its official statistics.

Statistics Show Value in Old‐School Crop Condition Reports

Statistics Show Value in Old‐School Crop Condition Reports

An assessment of USDA corn condition ratings across the U.S. Corn Belt

AbstractChanges in temperature, precipitation, and adverse weather across the U.S. Corn Belt associated with anthropogenic climate change may have an effect on corn (Zea mays L.) condition ratings in one of the leading corn production regions in the world. Using USDA National Agricultural Statistics Service data, this study aims to quantify the correlation of corn condition ratings with climate variables and yield, understand intergrowing season behavior of corn condition ratings, and quantify spatiotemporal trends of ratings across the U.S. Midwest (1986–2020). With an existing statistically significant (95% confidence level) correlation between corn condition ratings, Palmer drought indices, precipitation, temperature, and detrended corn yield, it is validated that qualitive rating data should be further examined. Using the Crop Condition Index (CCI), corn condition ratings supportive of positive yield prospects subtly decrease from June to September on average. The CCI values increased within June (ranging from 0.08 to 0.40 CCI yr–1),but subtly decreased within July and onward throughout the Midwest (ranging from –0.02 to –0.62 CCI yr–1 over the past 35 yr. From an interannual perspective, corn condition ratings supportive of extreme degrees of yield loss have significantly increased in 7 of the 12 Midwest states (ranging from 0.15 to 0.30% yr–1). Thus, increasing yields despite decreases in corn condition ratings suggests crop advancements such as hybrids and management have offset environment challenges with corn quality. These advancements need to be a continuing effort under a changing climate given rating trends, and corn condition ratings can provide essential risk assessment information for corn producers and other stakeholders.

Mississippi state and county level yield gap in corn production

AbstractContinuous corn (Zea mays L.) yield increases are required to promote economic development and support a larger population. Reducing the existing yield gaps is a potential strategy to accomplish this goal. The objective of this study was to evaluate yield trends, and gaps at different production levels in Mississippi using data from 2012 to 2021. Production levels considered were Mississippi yield contest (Yc), Mississippi State University hybrid testing trials under irrigation (Yp) and dryland (Yw), and actual yield (Ya) from USDA National Agricultural Statistics Service. Since 2012, Yc, Yp, and Ya are stagnant, and Yw has a nonsignificant positive trend. Averaged over 10 yr, a yield gap of 5.6 Mg ha−1 between Yc and Ya, 4.1 Mg ha−1 between Yp and Ya, and 2.0 Mg ha−1 between Yw and Ya were noted at state level. Existing yield gap underlines current production limitations and necessitates adoption of improved agronomic practices.Core Ideas In the last 10 yr, contest yield, potential yield, and actual yield in Mississippi have been stagnant. State level yield gap ranges between 2.0 to 5.6 Mg ha‐1 at different levels. Washington County showed the lowest (25.9%) and Lee County (48.8%) the highest yield gap in Mississippi.

Hybrid phenology matching model for robust crop phenological retrieval

Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.

Phenological corrections to a field-scale, ET-based crop stress indicator: An application to yield forecasting across the U.S. Corn Belt

Soil moisture deficiency is a major factor in determining crop yields in water-limited agricultural production regions. Evapotranspiration (ET), which consists of crop water use through transpiration and water loss through direct soil evaporation, is a good indicator of soil moisture availability and vegetation health. ET therefore has been an integral part of many yield estimation efforts. The Evaporative Stress Index (ESI) is an ET-based crop stress indicator that describes temporal anomalies in a normalized evapotranspiration metric as derived from satellite remote sensing. ESI has demonstrated the capacity to explain regional yield variability in water-limited regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to interannual phenological variability. This investigation selected three study sites across the U.S. Corn Belt – Mead, NE, Ames, IA and Champaign, IL – to investigate the potential operational value of 30-m resolution, phenologically corrected ESI datasets for yield prediction. The analysis was conducted over an 8-year period from 2010 to 2017, which included both drought and pluvial conditions as well as a broad range in yield values. Detrended yield anomalies for corn and soybean were correlated with ESI computed using annual ET curves temporally aligned based on (1) calendar date, (2) crop emergence date, and (3) a growing degree day (GDD) scaled time axis. Results showed that ESI has good correlations with yield anomalies at the county scale and that phenological corrections to the annual temporal alignment of the ET timeseries improve the correlation, especially when the time axis is defined by GDD rather than the calendar date. Peak correlations occur in the silking stage for corn and the reproductive stage for soybean – phases when these crops are particularly sensitive to soil moisture deficiencies. Regression equations derived at the time of peak correlation were used to estimate yields at county scale using a leave-one-out cross-validation strategy. The ESI-based yield estimates agree well with the USDA National Agricultural Statistics Service (NASS) county-level crop yield data, with correlation coefficients ranging from 0.79 to 0.93 and percent root-mean-square errors of 5–8%. These results demonstrate that remotely sensed ET at high spatiotemporal resolution can convey valuable water stress information for forecasting crop yields across the Corn Belt if interannual phenological variability is considered.

Agricultural Injuries among Farmers and Ranchers in the Central United States during 2011-2015

ABSTRACT The high risk of occupational fatalities in agriculture is well documented, but information on non-fatal injuries is lacking due to challenges in injury surveillance. This surveillance study explored the frequency, characteristics, and risk factors for non-fatal injuries among farmers and ranchers in the central United States. The Central States Center for Agricultural Safety and Health (CS-CASH), in collaboration with the USDA National Agricultural Statistics Service (NASS), conducted annual surveys (n = 34,777 sent) during 2011–2015 covering a seven-state region (Iowa, Kansas, Minnesota, Missouri, North Dakota, Nebraska, and South Dakota). The average response rate was 32% in the five consecutive annual surveys. The average injury incidence rate was 7.0 injuries/100 operators per year. Most injuries (89%) occurred during agricultural work. The most frequent sources of injury were livestock (22%), machinery (13%), and hand tools (12%). Risk factors for injury included: male gender, younger age (vs. 65+ years), farming as the primary occupation, greater work time, greater land area, ranch (vs. farm), organic farming, internet access, and production of several types of crops and animals. Most injuries (56%) required a doctor visit, and 12% required hospitalization. The average medical costs were $1,936 out of pocket and $8,043 paid by insurance. The combined average costs for most serious injuries were $7,858. Most injuries (66%) resulted in some lost time from agricultural work, and 13% were serious, resulting in more than 30 days of lost work time. The non-fatal injury rate for self-employed farmers and ranchers was higher than that of hired agricultural workers reported by the Bureau of Labor Statistics. This result reaffirms farming/ranching as a dangerous occupation and emphasizes the need for efforts to prevent agricultural injuries, especially those associated with identified injury sources and risk factors.

Economic estimates of invasive wild pig damage to crops in 12 US states

We report the results of a survey on invasive wild pig (Sus scrofa L.) damage and control in 12 US states (Alabama, Arkansas, California, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, and Texas). The crops chosen for this study represent the “second tier” in terms of economic importance after the six crops that were the subject of Anderson et al. (2016). The survey was distributed by the USDA National Agricultural Statistical Service (NASS) in the summer of 2019 to a sample of producers in each of the states (except California) of the following six crops: hay, pecans (Carya illinoinensis (Wangenh.) K.Koch), melons (cantaloupe (Cucumis melo L. var. cantalupensis), honeydew (Cucumis melo L. (Inodorus Group)), and watermelon (Citrullus Schrad.), sugarcane (Saccharum officinarum L.), sweet potatoes (Ipomoea batatas (L.) Lam.), and cotton (Gossypium L.). In California, where there the crops of economic importance differed from the other states in the study, damages were calculated for producers of hay, almonds (Prunus dulcis (Mill.) D.A. Webb), grapes (Vitis vinifera L.), sod, carrots (Daucus L.), lettuce (Lactuca L.), and strawberries (Fragaria L.). In total, 7438 respondents completed the questionnaire. Findings indicate that damage can be substantial. The highest yield loss estimates occurred for hay in Texas. Control efforts were common, but no control method was rated by the majority of producers as very effective. Extrapolating crop damage estimates to the state-level in 12 states with reportable damage yielded an estimated crop loss of $272 million/yr. Though large, this number likely represents only a small fraction of the total damage by wild pigs in these states because it only includes crop damage to six crops. We hope findings from this survey will help guide control efforts and research, as well as serve as a benchmark against which the effectiveness of future control efforts can be measured.

Predation and disease-related economic impacts of wild pigs on livestock producers in 13 states

We report the results of a survey on wild pigs (Sus scrofa) damage to livestock producers in 13 US states (Alabama, Arkansas, California, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas). The survey was distributed by the USDA National Agricultural Statistical Service in the summer of 2017 to a sample of livestock producers in the 13-state region. Findings indicate that predation and disease-related damage can be substantial in certain states and for certain types of livestock. In particular, damage to cattle operations in Texas and Arkansas was substantially higher than damage in other states and types of livestock operations. When extrapolated to livestock producers across the entire 13-state region, we estimated that damages sum to an annual cost of about $40 million. We hope findings from this survey will help guide control efforts and research, as well as serve as a benchmark against which the effectiveness of future control efforts can be measured.

Enhancing Crop Acreage Estimation within a Semiarid Watershed via Statistical Assessments and Techniques

Core Ideas We assessed methods to effectively reduce error in crop acreage estimates. The study compared statistical methods against National Agricultural Statistics Service data at the state and county level. We determined that improvements in crop estimates are largely affected by the input datasets. The error in crop acreage estimates is dependent on the crop, scope of analysis, and growing season. It is necessary to examine if the statistical methods designed to improve Cropland Data Layer (CDL) acreage estimates at a national level improve estimates at the county level. This study determined whether the calibration of CDL by statistical techniques increased the accuracy of crop estimates on a semiarid West Texas watershed. Since there is a substantial amount of agriculture and farming in areas like West Texas, accurate crop estimates of datasets at smaller scales become important. We compared three methods: the pixel counting method of the CDL, linear regression of the CDL against the MODIS Land Cover Type Product (MLCT), and the Bayesian method (posterior probability) on the National Land Cover Dataset (NLCD) against the USDA National Agricultural Statistical Service (NASS) Quick Stats. Four crops [corn (Zea mays L.), cotton (Gossypium hirsutum L.), sorghum (Sorghum bicolor [L.] Moench), and winter wheat (Triticum aestivum L.)] were analyzed at state and county levels during two growing seasons (2009 and 2011). Prediction errors were estimated by comparing computed acreage with the appropriate NASS Quick Stats values. The total error for each assessment method was estimated for all acreage values in that particular assessment category. We found that the Bayesian method improved the estimates of sorghum and winter wheat and linear regression improved acreage estimates for cotton and corn in 2011 and 2009, respectively. Our results indicate that county‐level adjustments are very sensitive. Therefore, one must carefully select the method that best fits the type of crop, geographical location, and growing season being considered.

Spatial Analysis of Stover Moisture Content During Harvest Season in the U.S.

Abstract. The moisture content of a maturing crop varies as the harvest season progresses. For crop residues such as corn stover, moisture content at the time of harvest can be as high as 75% (wet mass basis) to less than 20% depending on the geographic location (climate conditions) and stage of harvest. Biomass moisture content is critical for baling and extended storage. It is therefore essential to have an estimate of the quantities of corn stover available as wet or dry for various parts of the U.S. To this end, we analyzed hourly weather data (temperature, humidity, and rainfall) from the Typical Meteorological Year v.3 (TMY3) dataset developed by the National Renewable Energy Laboratory. A recently published set of equations for calculating the moisture content of stover as a function of hourly temperature, humidity, and rainfall were used. The annual start and end of corn grain harvest along with annual grain production (in bushels) for each state were extracted from USDA National Agricultural Statistics Service reports. Using these datasets and moisture sorption equations, the percentage of corn stover tonnage with moisture content less than 20%, between 20% and 40%, or greater than 40% was estimated from the length of time that the biomass was in these moisture content ranges. These calculations were carried out for several locations within each of the states for which TMY data were available. It was concluded that about 37.2% of corn stover is dry (<20% moisture content), whereas 36.5% is wet (>40% moisture content) nationwide. The remaining 27.0% of corn stover is between 20% and 40% moisture content. Keywords: Corn stover, Equilibrium moisture content, Field drying, Moisture content, Stover harvest, Typical Meteorological Year data.

Economic feasibility of cooling dry cows across the United States.

Heat stress during the dry period reduces milk yield in the subsequent lactation of dairy cows. Our objectives were to quantify the economic losses due to heat stress if dry cows are not cooled and to evaluate the economic feasibility of dry cow cooling. We used weather data from the National Oceanic and Atmospheric Administration to calculate the number of heat stress days for each of the 50 US states. A heat stress day was declared when the daily average temperature-humidity index was ≥68. The number of dairy cows in each state in 2015 was obtained from the USDA-National Agricultural Statistics Service. We assumed that 15% of the cows were dry at any time, a 60-d dry period, and a calving interval of 400d. Only cows in their second or greater parity (65%) benefitted from cooling during the dry period of the previous parity. Milk yield decreased by 5kg in the subsequent lactation (340d) if the cow experienced heat stress during the dry period based on a review of the literature. The default marginal value of milk minus feed cost was $0.33/kg of milk. The investment analysis included purchases of fans and soakers and use of water and electricity. Investment in a dry cow barn was considered separately. The average US dairy cow would experience 96 (26%) heat stress days during the year if not cooled and loses 447kg of milk in the subsequent lactation if not cooled when dry. Annual losses would be $810 million if dry cows were not cooled ($87/cow per yr). For the top 3 milk-producing states (California, Wisconsin, New York), and Florida and Texas, the average milk losses in the subsequent lactation were 522, 349, 387, 1,197, and 904kg, and reduced profit per cow per year would be $101, $68, $75, $233, and $176, respectively. The average benefit-cost ratio and payback periods of cooling dry cows in the United States were 3.15 and 0.27 yr (dry cow barn already present) and 1.45 and 5.68 yr (if investing in a dry cow barn) in the default scenario. To reach positive net present values, 6d (barn is present) and 55d (barn investment necessary) of heat stress annually were necessary (default assumptions). Other benefits of cooling, such as increased health and more productive offspring, were not considered. In conclusion, cooling of dry cows was profitable for 89% of the cows in the United States when building a new barn is required (under default assumptions) and very profitable when construction of a dry cow barn is not required (except for Alaska).

Factors affecting timing and intensity of calving season of beef cow-calf producers in the Midwest

Despite demonstrated market incentives to adopt controlled calving seasons, many producers still have herds that calve somewhat broadly throughout the year. We postulate this observed management behavior is related to a variety of factors. Primary data, collected through a coordinated survey effort with the USDA National Agricultural Statistics Service, were used to quantify factors that affect producers’ decisions regarding timing and intensity of calving season. Of the survey respondents, 88% were commercial producers and 12% were seedstock producers or operated a combination seedstock–commercial operation with an overall average of 63 beef cows. Ninety-seven, 50, 33, and 26% of farms calve in the spring (March, April, May), summer (June, July, August), fall (September, October, November), and winter (December, January, February), respectively. Twenty-two percent observed a calving season exclusively in the spring. Sixty-six percent of respondents indicated calving season was dictated by weather, 34% because of labor availability, and 31% because of tradition. Least often reasons for calving season were market timing (16%), feed availability (8%), and other (4%). Producer-stated reasons for calving season explained 62% of the variation in timing and intensity of calving on an operation, whereas a model of producer demographic and operation characteristics explained 83% of the variation. These results highlight the importance of evaluating producer and operation characteristics in addition to producer input when making recommendations to enhance production efficiency and profitability. Furthermore, understanding the factors that affect calving season provides opportunities for improved extension and research programming.

Economic estimates of feral swine damage and control in 11 US states

We report the results of one of the most comprehensive surveys on feral swine (Sus scrofa) damage and control in 11 US states (Alabama, Arkansas, California, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, South Carolina and Texas). The survey was distributed by the USDA National Agricultural Statistical Service in the summer of 2015 to a sample of producers of corn (Zea mays), soybeans (Glycine max), wheat (Triticum), rice (Oryza sativa), peanuts (Arachis hypogaea), and sorghum (Sorghum bicolor) in the 11-state region. Producers that failed to respond to the initial mailing received multiple follow-up phone calls in an attempt to minimize non-response bias, and a total of 4377 responses were obtained. Findings indicate that damage can be substantial. The highest yield loss estimates occur in peanut and corn production in the Southeast and Texas. Control efforts are common, and producers incur considerable costs from shooting and trapping efforts. Extrapolating crop damage estimates to the state-level in 10 states with reportable damage yields an estimated crop loss of $190 million. Though large, this number likely represents only a small fraction of the total damage by feral swine in the 10 states because it only includes crop damage to six crops. We hope findings from this survey will help guide control efforts and research, as well as serve as a benchmark against which the effectiveness of future control efforts can be measured.

A Survey of Feral Swine Damage in a Selection of U.S. States

We report the results of one of the most comprehensive surveys on feral swine damage and control in 11 U.S. states (Alabama, Arkansas, California, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, South Carolina, and Texas). The survey was distributed by the USDA National Agricultural Statistical Service in the summer of 2015 to a sample of producers of corn, soybeans, wheat, rice, peanuts, and sorghum in the 11 states listed above. A total of 4,377 responses were obtained. Findings indicate that damage caused by feral swine can be substantial. The highest yield loss estimates occur in peanut and corn production in the Southeast U.S. and Texas. We hope findings from this survey will help guide control efforts and research, as well as serve as a benchmark against which the effectiveness of future control efforts can be measured.