A common theme emerging from nutrient omission trials conducted extensively across sub-Saharan Africa has been the large variability in yield response to applied nutrients. Yet, little is known about the factors associated with this variability. Therefore, the objectives of this review were to (1) synthesize the available data from nutrient omission trials of maize and rice and quantify spatial variability in yield responses and the probability of attaining specified yield targets; (2) identify the genotypic, environmental, and nutrient management factors associated with yield responses; and (3) provide insights and actionable information for setting priorities for future nutrient management research and development. Here, we provide distributions and expected values of yield response and agronomic efficiencies of nitrogen (N), phosphorus (P), and potassium (K) to serve as benchmarks for improving fertilizer recommendations. We also show that with the combined application of NPK fertilizer, rain-fed maize and rice yields can be raised from the current yields of ~2 Mg ha −1 to 4 Mg ha −1 . Yield responses to N, P, and K were in the ratio of 11:6:1 in maize and 13:7:5 in rice, probably arising from differences in uptake requirements and growing environments. K yield responses were 2–3 times higher in rice than in maize. Explorative analysis using machine learning algorithms provided further insights into the possible interaction of agroecology, soil type, and exchangeable cations on the spatial variability in yield responses to N, P, and K in maize and rice. We recommend future research to address site-specific interactions between the applied and indigenous soil nutrients, focusing on optimizing application rates of K, other macronutrients and micronutrients for sustainable intensification of maize and rice production. This study highlights the critical need for balanced fertilization to optimize the productivity and nutrient use efficiencies in rice and maize production in smallholder farming systems in SSA.

research project (2014) we focused on the methodology followed to classify the outcome measures used in clinical trials in CF.Objectives: As a second step of the project we aimed at creating a full list and a classification of the outcome measures used in CF research, and a sub-list of endpoints that are perceived as particularly relevant by patients.Methods: Starting from the outcome measures used in each article included in the CFDB (www.cfdb.eu)we built a raw list of outcome measures.Each issue was disambiguated to eliminate synonyms and classified in the following categories: adverse effects, clinical, instrumental, mortality or survival, organisational, psycho-relational.We additionally classified the outcomes in patient-centered and patient-reported.Results: We found 685 outcome measures (some endpoints may be present in several categories).A small proportion of the outcomes used in CF research are patient-centered (PC) or patient-reported (PR): Adverse effects (PC:59%;PR:17%); Clinical (PC:17%;PR:0.5%);Instrumental (PC:4%; PR:0%); Mortality or survival (PC:100%;PR:0%); Organisational (PC:50%; PR:0%); Psycho-relational (PC:82%;PR:57%).As a whole PC and PR outcomes were 20% and 7%

Precision agriculture, or site-specific management, and environmental quality protection are inseparably linked. However, the impacts of precision agriculture on environmental quality has been poorly documented, and most scientists believe that judiciously applying agricultural inputs only when and where needed will reduce the impacts on the environment. This chapter will discuss the potential impacts of precision farming on the ecosystem.

Introduction The anti-tumour necrosis factor (TNF) agents, infliximab and adalimumab, are effective in treating inflammatory bowel diseases (IBD). Response is better in patients with detectable levels of circulating drug prior to the next administration (trough level). Some patients fail to respond to these drugs initially or lose response over time. Factors which influence loss of response are poorly defined but body mass index (BMI), prior surgery and smoking status have been reported to influence outcomes. In particular, BMI adversely affect outcomes with both infliximab and adalimumab, but the relationship of BMI to trough levels has not been reported previously. Method We conducted a single centre prospective cross-sectional study. All IBD patients treated with infliximab or adalimumab were eligible. Trough levels and anti-drug antibodies were obtained concurrently with routine clinical data, clinical markers of disease activity, faecal calprotectin (FC), C-reactive protein (CRP) and BMI. Variables including BMI that may predict trough levels and anti-drug antibodies were then assessed. Therapeutic infliximab was defined as trough level >2 mcg/ml (need units and adalimumab as >5.5 microgram/ml. Results 87 patients were included in the study; 69 had CD (44 IFX, 25 ADA) and 18 had UC (9 each on IFX and ADA). Of the total cohort, 12 had undetectable trough levels (<0.1) and 22 had supra-therapeutic levels (>8). A higher percentage of patients with undetectable trough levels were on infliximab (92% vs 8%, p < 0.01). Supra-therapeutic levels were noted in 55% and 45% of infliximab and adalimumab patients respectively. In the infliximab group, the median BMI was 24.9 (range 17.23–42.28), and 26 had aBMI >25 (49%). For adalimumab the median BMI was 22.92 (range 16.14–37.64), and 13 had a BMI >25 (38%). Those with sub-therapeutic trough levels had a higher BMI, which is most prominent in the adalimumab group. Patients with sub-therapeutic levels of infliximab had a median BMI of 25.3 (IQR: 22.6–30.5) compared to those with therapeutic levels 23.8 (IQR: 19.9–29.6). For adalimumab the median BMI was 22.8 (IQR: 20.5–25.6) in therapeutic and 27.4 (IQR: 21.9–32.9) in the sub-therapeutic group but this did not reach statistical significance. Conclusion There does appear to be a correlation between BMI and trough levels, particularly in patients receiving adalimumab. Therefore trough level measurement and dose adjustment, particularly in those taking adalimumab with a BMI >25, should be considered to optimise therapy and clinical outcomes. Disclosure of interest None Declared.

Abbreviations and notes: P = phosphorus; K = potassium; S = sulfur; Mg = magnesium; Zn = zinc; Cl= chloride; ppm = parts per million. Periodically, the International Plant Nutrition Institute (IPNI) summarizes data from public and private soil testing laboratories in North America. Laboratories provide data voluntarily, contributing their own staff time and computing resources. Summaries would not be possible without their generous contributions. This year marks the fourth summary using the same data collection protocol. Previous summaries were conducted in 2001, 2005, and 2010 (Fixen, 2002; Fixen, 2006; Fixen et al. 2010). The 2015 summary is not yet complete, and data continue to be submitted; however, the total number of samples collected for Corn Belt states is already substantially higher than previous summaries (Table 1). Until all data have been submitted, results are considered preliminary and the names of participating laboratories are being withheld. The protocol distributed to laboratories requested the numbers of samples in various soil test ranges. As in the 2010 summary, data were collected for P, K, S, Mg, Zn, Cl, and pH. Only preliminary data for P and K are presented. A total of 15 categories were used for P and 9 were used for K. Categories were unequal in width and were right-censored, with the highest category representing samples “greater than” the upper limit of the highest defi ned interval. Without censoring, a much larger number of categories would have been needed to characterize the few, very high levels characteristic of highly positively skewed soil test distributions. Different soil test ranges were used for various combinations of extractants and detection methods in an attempt to create equivalency in soil test calibration interpretation. Land Grant University Extension information was used where possible and scientifi c judgment was used to fi ll in knowledge gaps. Although equivalencies lacked scientifi c rigor, the same equivalencies were used in all summaries, giving credence to examining temporal trends. Because separate summaries were conducted each survey year, laboratory participation and total sample volume varied over time. Data presented in this publication use a subset of the protocol categories for P. The seven higher categories are grouped into the “>50” category. Data from the “31-40” category in the protocol were divided equally into “31-35” and “36-40” categories. Similarly, data in the “41-50” protocol category were divided equally into the “41-45” and “46-50” categories. These subdivisions were created to make it easier for the reader to visualize the distributions. In each survey year, laboratories were asked to contribute samples for that year’s cropping season rather than within standardized dates to allow for variation in sampling seasons across North America. However, many laboratories chose July By T.S. Murrell, P.E. Fixen, T.W. Bruulsema, T.L. Jensen, R.L. Mikkelsen, S.B. Phillips, and W.M. Stewart The Fertility of North American Soils: A Preliminary Look at 2015 Results

Many field experiments require the collection of forage yields in addition to grain yield (small grains). ln smal! plot research, mechanized grain harvest is common. However, forage harvest is often accomplished by hand which is time consuming and labor intensive. Sample heterogeneity increases when forage harvest is obtained by hand. The objectives of this study were to construct a mechanized forage harvester that would simplify the harvesting process while providing a homogenous sample and to determine forage yield reduction associated with using the harvester compared to hand clipping at the soil surface. A John Deere GT262 self propelled rotary mower was modified to be used for small plot wheat forage harvesting. Field trials were established at two locations to determine the yield reduction associated with using the forage harvester versus hand clipping at the soil surface. Significant differences in yield were found at both locations. The mean dry matter yield averaged over two years and two Iocations obtained using the harvester was 63.1% of that obtained by hand clipping at the soil surface during growth stages Feekes 6 and Feekes 10. Total N in wheat forage collected was significantly different using the harvester versus hand clipping. This is partially explained by decreased N in the lower stems at later stages of reproductive growth. The design of the harvester makes it possible to harvest several plots in a short amount of time while also allowing a larger area to be harvested which increases experimental accuracy and sample homogeneity. When the efficiency of the forage harvester (0.631) is used as a correction factor, the difference between the estimated and actual amounts of forage present is S 4o/" of the total forage present (Feekes 6 through Feekes 10).

Hulless barley (Hordeum vulgare L.) is higher in energy density and protein than hulled barley but management recommendations for this new crop are lacking. Intensively managed hulled barley receives two spring nitrogen (N) applications. How this will affect winter hulless barley yield and protein is unknown. 'Doyce' hulless barley was planted following corn (Zea mays L.) at seven site-years in the Coastal Plain of Virginia from 2005 to 2007. Spring N was applied as urea ammonium nitrate (UAN, 30% N) in an incomplete factorial of treatments at Zadoks growth stage (GS) 25 and 30 broadcast at rates of 0, 45, 67, and 89 kg N ha(-1). Six kg ha(-1) of phosphorus (P) was foliar applied at GS 30 to treatments receiving 45: 45 (45 kg N ha(-1) at GS 25 and 45 kg N ha(-1) at GS 30) and 45: 67 N splits. Additionally, 34 kg N ha(-1) as UAN was applied at GS 45 to treatments previously receiving either 45:45 or 45:67. Grain test weight increased with increasing N rate verifying that high spring N rates can be applied without negatively impacting grain test weight in this environment. Grain protein responded positively to increasing N rates as additional N at GS 45 increased protein by 0.7%. To maximize yields, 112 kg N ha(-1) applied as 45 kg N ha(-1) at GS 25 and 67 kg N ha(-1) at GS 30 was necessary. Supplying the majority of spring N need at GS 30 proved important to matching barley N demand and achieving high yields.

More time and research has been devoted to understanding N than any other nutrient. It is the most limiting nutrient in non-legume cropping systems and the least predictable. Mismanagement of N fertilizer can impact both economic and environmental aspects of crop production. Available soil N and yield level are determinants of a crop’s N requirement and are essential parameters to quantify optimal N application rates. Making precise N prescriptions are difficult because tremendous variability exists for available soil N and yield across time and space. Several destructive and non-destructive methods have been tested and established to assist in making midseason N fertilization rate decisions for rice. The chlorophyll meter and leaf color chart are among the tools that were developed to monitor rice N status (Peng et al., 1993; Stevens and Hefner, 1999). Nitrogen use efficiency was increased when in-season, sensor-based estimates of yield potential and crop responsiveness to N fertilization were used to determine midseason N rate for corn and wheat (Raun et al., 2002; Tubana et al., 2008). A study was initiated in 2008 at different sites in Louisiana and Mississippi to build a database required for the development of similar decision tool for rice. The database consists of grain yield and NDVI readings of three different rice varieties (Catahoula, Neptune, and CL151), which were collected at different growth stages from plots that received varying amounts of preflood N.

Normalized difference vegetation index (NDVI) measurements have the potential to improve mid-season N crop management decisions in rice (Oryza sativa L.). The objectives of this study were to determine the optimum sensing timing and establish a yield prediction model using NDVI measurements acquired with the GreenSeeker sensor. Weekly sensor readings were collected over a 5-wk period from multi-rate N fertilization trials established at six different locations from 2008 to 2010. Categorizing sensing timing by growth stage demonstrated that late sensing timings beyond panicle differentiation (PD) were impractical and reduced yield potential estimation as opposed to panicle initiation (PI) and PD timings. Regression analysis produced two viable yield potential prediction equations at PI (r(2) = 0.36) and PD (r(2) = 0.42). When sensor timings were categorized by cumulative growing degree days (GDD), 1301 to 1500 and > 2100 GDD groupings (r(2) = 0.28 and 0.37, respectively) were found to be inferior yield predictors as compared with 1501 to 1700 and 1701 to 1900 GDD groupings (r(2) = 0.41 for both). In almost all instances, normalization of NDVI data using days from seeding (DFS; NDVI/DFS) or GDD (NDVI/GDD) did not improve yield potential prediction as compared with NDVI alone. Yield potential, response index, and N response to fertilization are the three major components needed to produce a working algorithm capable of predicting mid-season N fertilization needs in rice. The four yield prediction models gleaned from this study provide the yield potential component for this algorithm. Multiple yield prediction models give crop managers freedom to select a model based on either physical growth stage or by accumulated GDD units.

Variable rate nitrogen (N) application based on in-season remote sensing can potentially improve wheat (Triticum aestivum L.) N management and N use efficiency (NUE). The goal of this study was to evaluate the potential of improving in-season soft red winter wheat (SRWW) variable rate N recommendations based on crop canopy reflectance. Small-plot N rate response calibration studies guided development of the Virginia Wheat Algorithm (VWA) for grain yield prediction and variable rate N fertilizer rate determination for SRWW. Large plot, replicated validation studies conducted for 15 site-years included an N-rich strip installed at growth stage (GS) 25 and various treatments at GS 30; four or five fixed-rate treatments applied to evaluate site N response, a variable rate based on the VWA applied using a GreenSeeker® RT 200 system and a “standard” fixed rate based on GS 30 wheat tissue N concentration. All sites responded positively to GS 30 N application. When data from one site were excluded, rates were 8 and 3 kg ha−1 below the economically optimal N rate (EONR) for the VWA and standard methods, respectively. Based on these data, the GreenSeeker® RT 200 system employing the VWA was equivalent to the current standard method and offers real-time rate prescriptions with less labor and less delay than the current tissue N concentration sufficiency standard.

Early-planted corn (Zea mays L.) generally has greater yield potential than later plantings in the Mid-Atlantic. However, cool, wet conditions early in the early season can delay emergence when corn is planted no-till resulting in lower yield compared to conventional tillage. Since uniform, vigorous stands are required to maximize corn yield, this research was undertaken to determine if stand establishment and yields for corn in the Mid-Atlantic Coastal Plain would benefit from in-row subsoil tillage when seeded at various depths. Experiments were conducted in 2004 and 2005 at two locations in Virginia. Main plots were no-till or in-row subsoiling using a no-till ripper with shanks 30 inches apart. Planting dates were two weeks earlier than normal, normal, or two weeks later than normal. On each date, corn was p0lanted into soybean stubble at depths of 0.5, 1.5, or 2.5 inches. In-row subsoiling increased grain yield in only one instance. Deeper planting (2.5 inches) resulted in higher grain moisture at harvest. Grain yields were maximized and risk of stand loss minimized by planting at 1.5 inches early or at the normal time. Planting early at 2.5 inches resulted in lower grain yield in two site years and generally delayed emergence. No consistent benefit from in row subsoil tillage was noted on these sandy soils.

Ethephon [(2-chloroethyl) phosponic acid] is commonly used in mid-Atlantic barley production to reduce plant height and lodging, but is known to reduce grain yield and test weight in some barley cultivars. Management practices that produce high yield and good grain quality in traditional mid-Atlantic winter barley have yet to be evaluated for hulless barley. This research examined response of three winter hulless barley genotypes to ethephon plant growth regulator (PGR) and provides recommendations for appropriate PGR rates. Ethephon applied at 2.5 oz a.i./acre resulted in decreased plant height (8.3 inches) and lodging index (1.2 units). With higher rates, a trend toward continued decreased height and lodging was seen even when differences were not significant. Grain yield was decreased by an average of 20 bu/acre in four of 15 site year-genotype combinations. Grain test weight was increased in one instance but impacts of ethephon varied by genotype, which may warrant further study. Labeled rates of ethephon are appropriate for application to a range of hulless barley cultivars but rates above 2.5 oz a.i./acre should be avoided unless severe lodging is anticipated. Ethephon application should occur only when moisture and temperature are favorable for plant growth and not when the crop is under stress.

A proposed Yield Reserve Program designed to compensate farmers for any reduced yields resulting from nitrogen (N) application rates reduced to below recommended rates is evaluated. Assuming that farmers currently follow Extension recommendations for applying N, Yield Reserve Program participation reduces expected net revenue by $10 to $13/ha. The Yield Reserve Program reduces expected net revenue by $17 to $20/ha for farmers who apply N to maximize expected net revenue. Farmers' costs of participation increase with lower probabilities of inadequate rainfall and higher corn prices and decline with higher N prices. The Yield Reserve Program can significantly reduce N applications to cropland, which may reduce N content of surface waters, but the costs to taxpayers and farmers will depend on how the program is implemented.