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We studied the effects of potassium (K) and its interactions with nitrogen (N), phosphorus (P) and harvest time on the productivity, water use efficiency (WUE) and radiation use efficiency (RUE) of cassava under rain-fed conditions. A field experiment was conducted during two consecutive years on K-deficient soils in Djakakope and on relatively K-rich soils in Sevekpota in Southern Togo, West Africa. Fifteen fertiliser combinations involving K and N rates of 0, 50 and 100 kg ha −1 each, and P rates of 0, 20 and 40 kg ha 1 were tested. Monthly measurements of leaf area index from 3 to 11 months after planting and daily weather data were used to estimate light interception, RUE, potential water transpiration and WUE of cassava. Overall WUE was 3.22 g dry matter kg −1 water transpired and RUE was 1.16 g dry matter MJ −1 intercepted photosynthetic active radiation (PAR). On the K-deficient soils, application of K increased WUE and RUE by 36–41% compared with 2.81 g dry matter kg −1 water transpired and 0.92 g dry matter MJ −1 intercepted PAR achieved without K, respectively. However, the effect of K on cassava growth depended on N availability. Applications of N had relatively weak effects on RUE and WUE, but induced a positive correlation between RUE/WUE and K mass fractions in the plant, and increased the cumulative amount of light intercepted by 11–51%, and the cumulative amount of water transpired through increased leaf area by 13–61%. No significant effect of P on WUE and RUE was observed. Increased cassava yields could be achieved under rain-fed conditions in West Africa through enhanced K management to increase RUE and WUE, along with sufficient N supply for improved light interception and water transpiration by the crop. [ABSTRACT FROM AUTHOR]

Abstract: The process of crop modelling to develop operational software requires different skills, from conceptualization of the biophysical system to computer programming, involving three main scientific disciplines: agronomy, mathematics, and software engineering. Model building implies transforming a conceptual model into sets of mathematical equations and then translating these equations into a computer program. Although recent crop modelling frameworks can technically support model building, the modelling process is not always well documented and difficult to repeat. The focus of this paper is therefore on developing and documenting an approach to re-assemble crop models, i.e. develop a new model from an existing one, using a crop modelling framework and crop physiological knowledge. Modifications to an initial crop model were classified according to three categories: (i) changes in parameter values, (ii) changes in equations, and (iii) changes in overall model structure. We illustrate the approach with a case study transforming a wheat crop model into a pea crop model. We discuss the role of each actor in the process to document diverse uncertainties related to the model (i.e. contextual situation, data, structure), and the general applicability of the approach for different crop modelling frameworks. We conclude that the use of our approach to re-assemble a crop model within a modelling framework facilitates integration of different disciplines around a modelling objective, and facilitates creating transparent and reproducible models. [Copyright &y& Elsevier]

We describe a summary model for the dynamics of carbon and nitrogen under varying weather, crop and soil conditions to investigate the role of soil organic carbon and nitrogen in yield formation in rice-based cropping systems of the Indo-Gangetic Plains (IGP). The model consists of three modules: soil organic matter (SOM), soil (SOIL), and crop growth (CROP). The SOM module consists of three pools: fresh, labile and stable. Carbon and nitrogen dynamics in the model are described in terms of carbon turnover, assimilation and dissimilation, with nitrogen linked through distinct C/N ratios. The SOIL module has two soil layers of variable depth (often 15cm each), and the labile and stable pools are replicated for these two layers. Water, crop and soil management practices and aeration characteristics of these layers affect the C and N dynamics of their respective pools. The CROP module calculates potential and actual biomass, leaf area index (LAI), evapotranspiration, total N demand, actual N uptake and N stress. Actual biomass is first partitioned between above (leaves, stem and storage organs) and below ground (roots and rhizodeposition), and a harvest index (HI) is applied to the aboveground biomass to calculate the yield. Independent data sets of 9 long-term experiments (LTEs) from the IGP of India were used for calibration and validation of the model. After calibration and validation, the model was used to explore the impact of various crop and soil management practices in rice-based cropping systems on different sites of the IGP. Overall, the model satisfactorily reproduced observed crop yields, SOC dynamics and total soil N dynamics for various cropping systems at different sites. Nitrogen mineralized from soil organic nitrogen contributed 5–75% to total N uptake in fertilized and non-fertilized treatments. With a recommended NPK application, a trend of significant decrease in simulated rice yield was found only at Ludhiana-3, and Pantnagar. Similarly, the simulated wheat yield also showed a significant trend of decline at Ludhina-3, Pantnagar and Barrackpore. Simulated SOC had a significant decline at Pantnagar, whereas at Ludhiana-3, it showed a significant increase. An increase in SOM is not always associated with an increase in yield, as the factor(s) improved by an increase in SOM may not be the limiting factor for crop growth. Depletion of many major macro (NPK) and micro (Zn, Fe, Mn, etc.) nutrients, essential for plant growth even with a balanced NPK fertilizer application and a maintained SOC, can lead to a decline in productivity. [Copyright &y& Elsevier]

Abstract: Policy decisions are often taken at the regional scale, while crop models, supporting these decisions, have been developed for individual locations, expecting location-specific, spatially homogeneous input data. Crop models are able to account for the variation in climatic conditions and management activities and their effects on crop productivity. However, regional applications require consideration of spatial variability in these factors. Several studies have analyzed effects of using spatially aggregated climate data on model outcomes. The effects of spatially aggregated sowing dates on simulations of crop phenological development have not been studied, however. We investigated the impact of spatial aggregation of sowing dates and temperatures on the simulated occurrence of ear emergence and physiological maturity of winter wheat in Germany, using the phenological model of AFRCWHEAT2. We observed time ranges for crop emergence exceeding 90d, whereas for harvesting this was more than 75d. Spatial aggregation to 100km×100km reduced this range to less than 30 and 20d for emergence and harvest, respectively. Differences among selected regions were relatively small, suggesting that non-climatic factors largely determined the spatial variability in sowing dates and consecutive phenological stages. Application of the AFRCWHEAT2 phenology model using location-specific weather data and emergence dates, and an identical crop parameter set across Germany gave similar deviations in all studied regions, suggesting that varietal differences were less important among regions than within regions. Importantly, bias in model outcomes as a result of using aggregated input data was small. Increase in resolution from 100km to 50km resulted in slight improvements in the simulations. We conclude that using spatially aggregated weather data and emergence dates to simulate the length of the growing season for winter wheat in Germany is justified for grid cells with a maximum area of 100km×100km and for the model considered here. As spatial variability of sowing dates within a region or country can be large, it is important to obtain information about the representative sowing date for the region. [Copyright &y& Elsevier]

Crop simulation models are widely applied at large scale for climate change impact assessment or integrated assessment studies. However, often a mismatch exists between data availability and the level of detail in the model used. Good modelling practice dictates to keep models as simple as possible, but enough detail should be incorporated to capture the major processes that determine the system's behaviour. The objective of this study was to investigate the effect of the level of detail incorporated in process-based crop growth models on simulated potential yields under a wide range of climatic conditions. We conducted a multi-site analysis and identified that by using a constant radiation use efficiency (RUE) value under a wide range of climatic conditions, the description of the process of biomass production may be over-simplified, as the effects of high temperatures and high radiation intensities on this parameter are ignored. Further, we found that particular attention should be given to the choice of the light interception approach in a crop model as determined by leaf area index (LAI) dynamics. The two LAI dynamics approaches considered in this study gave different simulated yields irrespective of the characteristics of the location and the light interception approaches better explained the differences in yield sensitivity to climatic variability than the biomass production approaches. Further analysis showed that differences between the two LAI dynamics approaches for simulated yields were mainly due to different representations of leaf senescence in both approaches. We concluded that a better understanding and modelling of leaf senescence, particularly its onset, is needed to reduce model uncertainty in yield simulations. [Copyright &y& Elsevier]

Abstract: An agricultural land use system centred on rice-based cropping systems as common in the Indo-Gangetic Plains (IGP), with its annual cycles of wet and dry, puddling and ploughing, is unique and exerts a specific influence on soil organic matter (SOM) dynamics. Reports of yield ‘stagnation’ in some parts of the IGP with a decline in SOM quantity and quality raises concerns about the sustainability of the rice–wheat system in the region. Proper understanding of the soil carbon balance and of measures required to build up or maintain the soil carbon status of such a production system is therefore important for its sustainable production. Long-term experiments conducted in this region are especially useful in gaining understanding of soil carbon dynamics, since the processes affecting carbon dynamics are slow in nature. We used a simple analytical model—Yang''s model—to calculate carbon balances in the rice-based cropping systems of the IGP in India. We used eight data sets from rice-based cropping systems from different sub-regions in the IGP, with different crop managements applied to rice, wheat or a third crop. Carbon input into the soil from crop biomass was calculated using data on crop yield and Harvest Index (HI). The values of soil organic carbon content predicted by the model were comparable to the observed values (r=0.91). The model performs well in situations with porous soils (low clay content), with a pH values in the neutral range (7–7.5) and low annual rainfall as in the situation of Ludhiana-1 and 2. However, it underperforms in situations with heavy clay soils with high rainfall, causing severe anaerobic conditions. The model projections for the long-term (by 2080) show a decline in SOC at all sites in the IGP. Hence, the yield stagnation in the IGP, which has been attributed to a decline in SOC and the associated reduction in nutrient supply, could lead to further decreases in SOC levels, aggravated by climate change-induced higher temperatures. [Copyright &y& Elsevier]

Abstract: Crop growth models are used for a wide range of objectives. For each objective a specific model has to be developed, because the reusability of a model is often limited by the necessity of a fundamental restructuring to adapt it to a different objective. To overcome this limitation, we developed a method to facilitate model restructuring by a novel combination of software technology with expert knowledge. This resulted in the decision-making software application CROSPAL (CROp Simulator: Picking and Assembling Libraries). CROSPAL includes (1) a library of processes each containing different modelling approaches for each crop physiological process and (2) a procedure based on expert knowledge of how to combine the different processes for the objective of the simulation. A brief overview of the state of the art in crop modelling is presented, followed by an account of the developed concept to improve flexibility in crop modelling considering expert knowledge. We describe the design of the software and how expert knowledge is integrated. The use of CROSPAL is illustrated for the modelling of crop phenology. We conclude that CROSPAL is a helpful tool to improve flexibility in crop modelling considering expert knowledge but further development and evaluation is required to extend its range of application to more processes and issues crop modelling is presently addressing. [Copyright &y& Elsevier]

Abstract: Recently, regular oscillations in bacterial populations and growth rates of bacterial feeding nematodes (BFN) were shown to occur after addition of fresh organic matter to soil. This paper presents a model developed to investigate potential mechanisms of those oscillations, and whether they were initiated by bacteria–substrate interactions or by predatory regulation of bacteria. The model was also used to investigate mineral nitrogen release during short-term organic matter decomposition. Experimental data originated from several microcosm experiments with a sandy soil amended with clover–grass mixture. Numbers of bacteria and BFN, and nitrate and ammonium content in soil were measured daily during about a month, whereas protozoa were counted occasionally. A substrate-based food web model was constructed with 3 plant residue and 5 soil organic matter compartments, 3 trophic groups of bacteria (copiotrophic, oligotrophic and hydrolytic), and two predatory groups (BFN and protozoa). Both carbon and nitrogen flows between these compartments were modelled. Fluctuations in microbial populations in soil after plant residue incorporation could be reproduced with and without participation of predators. The first two peaks in bacterial numbers were mainly related to bacteria–substrate interactions, while predators (particularly protozoa) influenced bacterial dynamics during later stages of bacterial community development. Oligotrophic bacteria had a stabilizing effect on fluctuations of other trophic groups, and were the main source of nutrients for predators. A peak in soil ammonium occurred within 1 week after residue incorporation. Nitrate increased sigmoidally after a short lag phase. The final nitrate concentration was primarily determined by bacterial dynamics, and to a lesser extent by protozoa and nematodes. This model indicates the importance of substrate–consumer relations for regulation of populations at different trophic levels and nutrient release from fresh organic matter added to soil. [Copyright &y& Elsevier]

Abstract: Strong gradients of decreasing soil fertility are found with increasing distance from the homestead within smallholder African farms, due to differential resource allocation. As nutrient use efficiency varies strongly along these gradients, such heterogeneity must be considered when designing soil management strategies, aimed at an improved overall resource use efficiency at farm scale. Here, we quantify the magnitude and study the origin of farmer-induced, within-farm soil fertility gradients as affected by biophysical and socio-economic conditions, and investigate farmers’ perceptions of such heterogeneity. Farm transects, participatory resource flow mapping, farmers’ classification of land qualities, and soil sampling for both chemical and spectral reflectance analyses were performed across 60 farms in three sub-locations (Emuhaia, Shinyalu, Aludeka) representing the variability found in the highlands of western Kenya. Differences between the various field types of a farm were observed for input use (e.g. 0.7–104kg Nha−1), food production (e.g. 0.6–2.9t DMha−1), partial C (e.g. −570 to 1480kgha−1) and N (e.g. −92 to 57kgha−1) balances and general soil fertility status, despite strong differences across sub-locations. Concentration of nutrients in the home fields compared with the remote fields were verified for extractable P (e.g. 2.1–19.8mgkg−1) and secondarily for exchangeable K (e.g. 0.14–0.54cmol(+) kg−1), on average, whereas differences for soil C and N were only important when considering each individual farm separately. Farmers managed their fields according to their perceived land quality, varying the timing and intensity of management practices along soil fertility gradients. Fields classified by them as poor were planted later (up to 33.6 days of delay), with sparser crops (ca. 30% less plants m−2) and had higher weed infestation levels than those classified as fertile, leading to important differences in maize yield (e.g. 0.9 versus 2.4tha−1). The internal heterogeneity in resource allocation varied also between farms of different social classes, according to their objectives and factor constraints. Additionally, the interaction of sub-location-specific socio-economic (population, markets) and biophysical factors (soilscape variability) determined the patterns of resource allocation to different activities. Such interactions need to be considered for the characterisation of farming system to facilitate targeting research and development interventions to address the problem of poor soil fertility. [Copyright &y& Elsevier]

Abstract: The processes of nutrient depletion and soil degradation that limit productivity of smallholder African farms are spatially heterogeneous. Causes of variability in soil fertility management at different scales of analysis are both biophysical and socio-economic. Such heterogeneity is categorised in this study, which quantifies its impact on nutrient flows and soil fertility status at region and farm scales, as a first step in identifying spatial and temporal niches for targeting of soil fertility management strategies and technologies. Transects for soil profile observation, participatory rural appraisal techniques and classical soil sampling and chemical analysis were sampled across 60 farms in three sub-locations (Emuhaia, Shinyalu, Aludeka), which together represent much of the variability found in the highlands of western Kenya. Five representative farm types were identified using socio-economic information and considering production activities, household objectives and the main constraints faced by farmers. Soil fertility management and nutrient resource flows were studied for each farm type and related to differences in soil fertility status at farm scale. Farm types 1 and 2 were the wealthiest; the former relied on off-farm income and farmed small pieces of land (0.6–1.1ha) while the latter farmed relatively large land areas (1.6–3.8ha) mainly with cash crops. The poorest farm type 5 also farmed small pieces of land (0.4–1.0ha) but relied on low wages derived from working for wealthier farmers. Both farm types 1 and 5 relied on off-farm earnings and sold the least amounts of farm produce to the market, though the magnitude of their cash, labour and nutrient flows was contrasting. Farms of types 3 and 4 were intermediate in size and wealth, and represented different crop production strategies for self-consumption and the market. Average grain yields fluctuated around 1tha−1 year−1 for all farm types and sub-locations. Grain production by farms of types 4 and 5 was much below annual family requirements, estimated at 170kgperson−1 year−1. Household wealth and production orientation affected the pattern of resource flow at farm scale. In the land-constrained farms of type 1, mineral fertilisers were often used more intensively (ca. 50kgha−1), though with varying application rates (14–92kgha−1). The use of animal manure in such small farms (e.g. 2.2t year−1) represented intensities of use of up to 8tha−1, and a net accumulation of C and macronutrients brought into the farm by livestock. In farms of type 5, intensities of use of mineral and organic fertilisers ranged between 0–12kgha−1 and 0–0.5tha−1, respectively. A consistent trend of decreasing input use from farm types 1–5 was generally observed, but nutrient resources and land management practices (e.g. fallow) differed enormously between sub-locations. Inputs of nutrients were almost nil in Aludeka farms. Both inherent soil properties and management explained the variability found in soil fertility status. Texture explained the variation observed in soil C and related total N between sub-locations, whereas P availability varied mainly between farm types as affected by input use. [Copyright &y& Elsevier]

Maize is the main grain crop grown in the highlands of sub-Saharan Africa, on a broad range of soil fertility and management conditions. Important yield variability has been reported at different scales, reflecting the intensity and spatial distribution of growth-limiting and growth-reducing factors. Maize yield estimation represents a valuable tool to assess within-farm variability in soil fertility through crop performance. The objective of this study was to develop mathematical relationships between plant morphological attributes and grain yield of tropical maize genotypes, based on plant allometric characteristics. These models were used to estimate maize yields and the estimates were validated against independent data collected from experimental and farmers’ fields in western Kenya. Three commercial hybrids and three local varieties were considered. Multiple linear regression models including plant height and either ear length or ear diameter as explanatory variables, and simple linear regressions including only plant height, were the most accurate to estimate both total aboveground biomass and grain dry matter yields per plant (r2: 0.76–0.91). Average values for the harvest index ranged between 0.34 and 0.42, varying with the total aboveground biomass produced per plant. Yield estimations on ground area basis for farmers’ fields were acceptably accurate. Plant height measurements can be easily taken at any moment after maize flowering and used in simple models to estimate maize yield. This approach proved also a valuable tool to discuss yield variability with farmers. [Copyright &y& Elsevier]

Abstract: Weather data are essential inputs for crop growth models, which are primarily developed for field level applications using site-specific daily weather data. Daily weather data are often not available, especially when models are applied to large regions and/or for future projections. It is possible to generate daily weather data from aggregated weather data, such as average monthly weather data, e.g. through a linear interpolation method. But, due to the nonlinearity of many weather–crop relationships, results of simulations using linearly interpolated data will deviate from those with actual (daily) data. The objective of this study was to analyse the sensitivity of different modelling approaches to the temporal resolution of weather input data. We used spring wheat as an example and considered three combinations of summarized and detailed approaches to model leaf area index development and associated radiation interception and biomass productivity, reflecting the typical range of detail in the structure of most models. Models were run with actual weather data and with aggregated weather data from which day-to-day variation had been removed by linear interpolation between monthly averages. Results from different climatic regions in Europe show that simulated biomass differs between model simulations using actual or aggregated temperature and/or radiation data. In addition, we find a relationship between the sensitivity of an approach to interpolation of input data and the degree of detail in that modelling approach: increasing detail results in higher sensitivity. Moreover, the magnitude of the day-to-day variability in weather conditions affects the results: increasing variability results in stronger differences between model results. Our results have implications for the choice of a specific approach to model a certain process depending on the available temporal resolution of input data. [Copyright &y& Elsevier]

Abstract: Fluxes of methane (CH4) and carbon dioxide (CO2) estimated by empirical models based on small-scale chamber measurements were compared to large-scale eddy covariance (EC) measurements for CH4 and to a combination of EC measurements and EC-based models for CO2. The experimental area was a flat peat meadow in the Netherlands with heterogeneous source strengths for both greenhouse gases. Two scenarios were used to assess the importance of stratifying the landscape into landscape elements before up-scaling the fluxes measured by chambers to landscape scale: one took the main landscape elements into account (field, ditch edge ditch), the other took only the field into account. Non-linear regression models were used to up-scale the chamber measurements to field emission estimates. EC CO2 respiration consisted of measured night time EC fluxes and modeled day time fluxes using the Arrhenius model. EC CH4 flux estimate was based on daily averages and the remaining data gaps were filled by linear interpolation. The EC and chamber-based estimates agreed well when the three landscape elements were taken into account with 16.5% and 13.0% difference for CO2 respiration and CH4, respectively. However, both methods differed 31.0% and 55.1% for CO2 respiration and CH4 when only field emissions were taken into account when up-scaling chamber measurements to landscape scale. This emphasizes the importance of stratifying the landscape into landscape elements. The conclusion is that small-scale chamber measurements can be used to estimate fluxes of CO2 and CH4 at landscape scale if fluxes are scaled by different landscape elements. [Copyright &y& Elsevier]