• Authors:
    • Johnston, A.
    • Snapp, S.
    • Zingore, S.
    • Chikowo, R.
  • Source: Nutrient Cycling in Agroecosystems
  • Volume: 100
  • Issue: 1
  • Year: 2014
  • Summary: Farm typologies are a useful tool to assist in unpacking and understanding the wide diversity among smallholder farms to improve targeting of crop production intensification strategies. Sustainable crop production intensification will require the development of an array of nutrient management strategies tailored to farm-specific conditions, rather than blanket recommendations across diverse farms. This study reviewed key literature on smallholder farm typologies focusing on three countries (Kenya, Malawi and Zimbabwe), to gain insights on opportunities for crop production intensification, and the importance of developing farm-specific nutrient management practices. Investigations on farm typologies have done well in highlighting the fundamental differences between farm categories, with 3-5 typologies often adequate to represent the wide differences in resource endowment. Resource-endowed farmers have ready access to large quantities of manure and mineral fertilizers, which contribute to higher soil fertility and crop productivity on their farms. Resource-constrained households use little or no manure and mineral fertilizers, and have limited capacity to invest in labour-demanding soil fertility management technologies. These farmers often have to rely on off-farm opportunities for income that are largely limited to selling unskilled labour to their resource-endowed neighbors. The variability in management practices by farmers has resulted in three main soil fertility classes that can be used for targeting soil fertility management technologies, characterized by potential response to fertilizer application as: (1) low-responsive fertile fields that receive large additions of manure and fertilizer; (2) high-responsive infertile fields that receive moderate nutrient applications; (3) poorly responsive degraded soils cultivated for many years with little or no nutrient additions. The main conclusions drawn from the review are: (1) resource constrained farmers constitute the widest band across the three countries, with many of the farmers far below the threshold for sustainable maize production intensification and lacking capacity to invest in improved seed and fertilizer, (2) farm sizes and livestock ownership were key determinants for both farmer wealth status and farm productivity, and (3) soil organic carbon and available P were good indicators for predicting previous land management, that is also invariably linked to farmer resource endowment.
  • Authors:
    • Destain, J. P.
    • Destain, M. F.
    • Bodson, B.
    • Ferrandis, S.
    • Leemans, V.
    • Dumont, B.
  • Source: Precision Agriculture
  • Volume: 15
  • Issue: 3
  • Year: 2014
  • Summary: The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.
  • Authors:
    • Singh, B. P.
    • Fang, Y. Y.
    • Singh, B.
  • Source: Agriculture Ecosystems and Environment
  • Volume: 191
  • Year: 2014
  • Summary: Temperature sensitivity of biochar-C in soils is not well understood. To acquire this information, we incubated two delta 13C-depleted (-36.3 or -36.5 per mil) wood biochars produced at 450 and 550°C, under controlled laboratory conditions at 20, 40 and 60°C in four contrasting soils (Inceptisol, Entisol, Oxisol and Vertisol). The respired CO 2 and associated delta 13C were analysed periodically (12-22 times) over two years. The temperature sensitivity of biochar-C and native SOC mineralisation was computed as: (i) averaged Q 10 (Q 10a) for the whole (2-year) time series using a temperature-incorporated mineralisation model to estimate a temperature scaling function for the exponential Q 10 model; (ii) instantaneous Q 10 (Q 10i) by using a time series of C mineralisation rates for a simple Q 10 model; and (iii) cumulative Q 10 (Q 10c) by using cumulative C mineralised over certain incubation periods for a simple Q 10 model. The mineralisation rates of biochar-C and native SOC increased with increasing temperature and their temperature sensitivities were significantly ( p<0.001) affected by soil type. For example, biochar-C Q 10a was the greatest (also for native SOC) in the Vertisol (2.74-2.77), followed by Inceptisol (2.47-2.66) and Entisol (2.39-2.45), and the smallest in the Oxisol (1.93-2.20) for the 20-40°C range. Biochar and native SOC Q 10a were the smallest in the Vertisol for the 40-60°C range. Biochar-C Q 10a was not influenced by biochar type (450 or 550°C). The presence of biochar decreased Q 10a of the native SOC in the Entisol, Vertisol and Inceptisol, but this influence did not occur in the Oxisol, especially at 20-40°C. The temperature sensitivity of biochar-C (Q 10a and Q 10c) and SOC (Q 10a and Q 10i) decreased with increasing incubation temperature range. The Q 10i values of biochar-C and SOC increased with time in the 20-40°C range. Even though biochar-C was found to be more stable than native SOC (based on their mineralisation rate constants), the Q 10a, Q 10c and Q 10i values for biochar-C were either smaller or similar to that of native SOC. In conclusion, the findings of this study which was conducted in the absence of plant suggest that soil characteristics can alter the temperature sensitivity of biochar-C. Furthermore, biochar can decrease the temperature sensitivity of native SOC mineralisation and consequently enhance C sequestration in soil under climate warming.
  • Authors:
    • Carroll, J.
    • Burke, B.
    • Finnan, J.
  • Source: Nutrient Cycling in Agroecosystems
  • Volume: 98
  • Issue: 1
  • Year: 2014
  • Summary: Nitrogen fertilizer was applied to willow after harvest in 2011, two levels of nitrogen were applied (75; 150 kg N/ha) in addition to a control. The trial was harvested in January 2013, biomass from each treatment was burnt and emissions from combustion were quantified. Nitrogen application increased leaf nitrogen and plant height although there was no difference between the nitrogen treatments. Plant height and maximum stem diameter increased with applied nitrogen at final harvest. Nitrogen fertilization significantly increased yield by 35 % although there was no difference between the two nitrogen treatments. Stem nitrogen content did not differ significantly between treatments and there was no significant difference in NOx emissions between treatments. A life cycle assessment showed that nitrogen fertilization significantly increased net greenhouse gas benefit by up to 30 % depending on the fuel replaced. The study demonstrated that the application of relatively low levels of nitrogenous fertilizer to willow can significantly improve greenhouse gas mitigation without affecting other aspects of the environment such as air quality.
  • Authors:
    • Kuhnert, M.
    • Hastings, A.
    • Williams, J. R.
    • Smith, K.
    • Datta, A.
    • Fitton, N.
    • Topp, C. F. E.
    • Smith, P.
  • Source: Nutrient Systems in Agroecosystems
  • Volume: 99
  • Issue: 1-3
  • Year: 2014
  • Summary: Biogeochemical models such as DailyDayCent (DDC) are increasingly used to help quantify the emissions of green-house gasses across different ecosystems and climates. For this use they require parameterisation to represent a heterogeneous region or are site specific and scaled upwards. This requires information on inputs such as climate, soil, land-use and land management. However, each input has an associated uncertainty, which propagates through the model to create an uncertainty in the modelled outputs. To have confidence in model projections, an assessment of how the uncertainty in inputs propagated through the model and its impact on modelled outputs is required. To achieve this, we used a pre-defined uncertainty range of key inputs; temperature, precipitation, clay content, bulk density and soil pH, and performed a sensitivity and uncertainty analysis, using Monte Carlo simulations. This allowed the effect of measurement uncertainty on the modelled annual N2O emissions and crop yields at the Grange field experimental site to be quantified. Overall the range of model estimates simulated was relatively high and while the model was sensitive to each input parameter, uncertainty was driven by the sensitivity to soil pH. This decreased as the N fertiliser application rate increased, as at lower N application rates the model becomes more sensitive to other drivers of N mineralisation such as soil and climate inputs. Therefore, while our results indicate that DDC can provide a good estimate of annual N2O emissions and crop yields under UK conditions, reducing the uncertainty in the input parameters will lead to more accurate simulations.
  • Authors:
    • Richard, G.
    • Grossel, A.
    • Nicoullaud, B.
    • Cellier, P.
    • Rochette, P.
    • Henault, C.
    • Loustau, D.
    • Gu, J.
  • Source: Nutrient Cycling in Agroecosystems
  • Volume: 98
  • Issue: 1
  • Year: 2014
  • Summary: Modeling nitrous oxide (N2O) emissions from agricultural soils is still a challenge due to influences of artificial management practices on the complex interactions between soil factors and microbial activities. The aims of this study were to evaluate the process-based DeNitrification-DeComposition (DNDC, version 9.5) model and modified non-linear empirical Nitrous Oxide Emission (NOEV2) model with weekly N2O flux measurements at eight sites cropped with winter wheat across a tile-drained landscape (around 30-km(2)) in Central France. Adjustments of the model default field capacity and wilting point and the optimum crop production were necessary for DNDC95 to better match soil water content and crop biomass yields, respectively. Multiple effects of varying soil water and nitrate contents on the fraction of N2O emitted through denitrification were added in NOEV2. DNDC95 and NOEV2 successfully predicted background N2O emissions and fertilizer-induced emission peaks at all sites during the experimental period but overestimated the daily fluxes on the sampling dates by 54 and 25 % on average, respectively. Cumulative emissions were slightly overestimated by DNDC95 (4 %) and underestimated by NOEV2 (15 %). The differences between evaluations of both models for daily and cumulative emissions indicate that low frequency measurements induced uncertainty in model validation. Nonetheless, our validations for soil water content with daily resolution suggest that DNDC95 well represented the effect of tile drainage on soil hydrology. The model overestimated soil ammonium and nitrate contents mostly due to incorrect nitrogen partitioning when urea ammonium nitrate solution was applied. The performance of the model would be improved if DNDC included the canopy interception and foliar nitrogen uptake when liquid fertilizer was sprayed over the crops.
  • Authors:
    • Boettcher, J.
    • Kage, H.
    • Ratjen, A.
    • Heumann, S.
  • Source: Nutrient Cycling in Agroecosystems
  • Volume: 99
  • Issue: 1-3
  • Year: 2014
  • Summary: Eliminating uncertainty in soil N supply could reduce fertilizer input, but the amount of N mineralized during plant growth is usually still unknown. We aimed to test the relatively simple two-pool net N mineralization model NET N that uses site-specific temperature and soil water functions as well as pedotransfer functions for deriving the pool sizes and was developed for NW Germany. The objectives were to (1) evaluate, if field net N mineralization under unfertilized winter wheat could be satisfactorily simulated, and to (2) examine the variation in time patterns of net N mineralization within years and sites and from two functional N pools: a rather small, fast mineralizable N pool (N-fast) and a much greater, slowly mineralizable N pool (N-slow). NET N simulations for 36 site-year-combinations and up to five dates within the growing season were evaluated with detailed N balance approaches (calculated from: soil mineral N contents, plant N uptake using estimates of green area index, simulated N leaching). Simulated net N mineralization was highly significantly correlated (r(2) = 0.58; root mean square error = 24.2 kg N ha(-1)) to estimations from the most detailed balance approach, with total simulated net N mineralization until mid August ranging from 62.1 to 196.5 kg N ha(-1). It also became evident that N mineralization from pool N-slow-in contrast to pool N-fast-was considerably higher for loess soils than for sandy or loamy soils. The results suggest that NET N was adequate for simulations in unfertilized winter wheat. However, further field studies are necessary for proving its applicability under fertilized conditions.
  • Authors:
    • Kuhlmann, H.
    • Lammel, J.
    • Senbayram, M.
    • Lebender, U.
  • Source: Nutrient Cycling in Agroecosystems
  • Volume: 100
  • Issue: 1
  • Year: 2014
  • Summary: Nitrogen fertilizers are a major source of nitrous oxide (N2O) emissions from arable soils. The relationship between nitrogen application rates and N2O emissions was evaluated during the growth period of winter wheat (similar to 140 days) at six field sites in north-western Germany. Nitrogen was applied as calcium-ammonium-nitrate, with application rates ranging between 0 and 400 kg N ha(-1). One trial was conducted in 2010, three trials in 2011 and two trials in 2012. Additionally, post-harvest N2O emissions were evaluated at two field sites during autumn and winter (2012-2013). The emission factors (during the growth period) varied between 0.10 and 0.37 %. Annual N2O emissions ranged between 0.46 and 0.53 % and were consistently lower across all sites and years than to the IPCC Tier 1 default value (1.0 %). Across all sites and years, the relationship between N2O and N application rate was best described by linear regression even if nitrogen amounts applied were higher than the nitrogen uptake of the crop. Additionally, annual N2O emissions per unit of harvested wheat grain were calculated for two field sites to assess the environmental impact of wheat grain production. Yield-scaled N2O emissions followed a hyperbolic function with a minimum of 177 and 191 g N2O-N t grain yield(-1) at application rates of 127 and 150 kg N ha(-1), followed by an increase at higher N application rates. This relationship indicates that wheat crop fertilization does not necessarily harm the environment through N2O emissions compared to zero fertilization. Thus, improving nitrogen use efficiency may be the best management practice for mitigating yield-scaled N2O emissions.
  • Authors:
    • Mekuria, M.
    • Aune, J. B.
    • Johnsen, F. H.
    • Ngwira, A.
    • Thierfelder, C.
  • Source: Journal of Soil and Water Conservation
  • Volume: 69
  • Issue: 2
  • Year: 2014
  • Summary: Understanding factors affecting farmers' adoption of improved technologies is critical to success of conservation agriculture (CA) program implementation. This study, which explored the factors that determine adoption and extent of farmers' use of the three principles of CA (i.e., minimum soil disturbance, permanent soil cover with crop residues, and crop rotations), was conducted in 10 target communities in 8 extension planning areas in Malawi. The primary data was collected using structured questionnaires administered to individual households. Triangulation with key informant interviews, field observations, and interactive discussions with farmers and farmer groups provided information behind contextual issues underpinning the statistical inferences. From a total of 15,854 households in the study areas, it is estimated that 18% of the smallholder farmers had adopted CA, representing an area of about 678 ha (1,675 ac; 2.1% of all cultivated land). Land area under CA constituted about 30% of total cultivated land among adopters. A random sample of 151 adopters and 149 nonadopters proportional with respect to adoption rates was drawn from various communities and interviewed using structured questionnaires. A total of 30 key informant interviews were conducted with stakeholders including staff of Total Land Care, government extension workers, agroinput suppliers, and lead farmers. The first stage of the Heckman model showed that hired labor, area of land cultivated, membership to farmer group, and district influenced farmers' decisions to adopt CA. The second stage of Heckman model results suggested that total cultivated land, duration of practicing CA, and district influenced farmers' decisions to extend their land to CA. Our study can be used to show the agency and social structures that are likely to influence adoption and extent of CA. Future policy should address ways to provide access to information and long-term support to farmers to enable them to embrace the technology fully.
  • Authors:
    • Bui, E. N.
    • Webster, R.
    • Rossel, R. A. V.
    • Baldock, J. A.
  • Source: Global Change Biology
  • Volume: 20
  • Issue: 9
  • Year: 2014
  • Summary: We can effectively monitor soil condition - and develop sound policies to offset the emissions of greenhouse gases - only with accurate data from which to define baselines. Currently, estimates of soil organic C for countries or continents are either unavailable or largely uncertain because they are derived from sparse data, with large gaps over many areas of the Earth. Here, we derive spatially explicit estimates, and their uncertainty, of the distribution and stock of organic C in the soil of Australia. We assembled and harmonized data from several sources to produce the most comprehensive set of data on the current stock of organic C in soil of the continent. Using them, we have produced a fine spatial resolution baseline map of organic C at the continental scale. We describe how we made it by combining the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. Values of stock were predicted at the nodes of a 3-arc-sec (approximately 90 m) grid and mapped together with their uncertainties. We then calculated baselines of soil organic C storage over the whole of Australia, its states and territories, and regions that define bioclimatic zones, vegetation classes and land use. The average amount of organic C in Australian topsoil is estimated to be 29.7 t ha -1 with 95% confidence limits of 22.6 and 37.9 t ha -1. The total stock of organic C in the 0-30 cm layer of soil for the continent is 24.97 Gt with 95% confidence limits of 19.04 and 31.83 Gt. This represents approximately 3.5% of the total stock in the upper 30 cm of soil worldwide. Australia occupies 5.2% of the global land area, so the total organic C stock of Australian soil makes an important contribution to the global carbon cycle, and it provides a significant potential for sequestration. As the most reliable approximation of the stock of organic C in Australian soil in 2010, our estimates have important applications. They could support Australia's National Carbon Accounting System, help guide the formulation of policy around carbon offset schemes, improve Australia's carbon balances, serve to direct future sampling for inventory, guide the design of monitoring networks and provide a benchmark against which to assess the impact of changes in land cover, land management and climate on the stock of C in Australia. In this way, these estimates would help us to develop strategies to adapt and mitigate the effects of climate change.