Individual Field Level Research Articles

Herbicide resistance in Alopecurus myosuroides: The value of routine testing of seed samples submitted by farmers since 1985

AbstractIn 35 years of routine screening for herbicide resistance, a total of 3758 seed sample/herbicide combinations were assayed. Resistance testing was done in either pots in glasshouses (chlortoluron, fenoxaprop and mesosulfuron‐methyl + iodosulfuron‐methyl sodium) or Petri‐dishes in incubators (sethoxydim, cycloxydim and pendimethalin). With all herbicides, the relationship between herbicide efficacy and year of sampling was linear, with the slope representing the annual loss of efficacy. This was higher for the ALS inhibitors mesosulfuron + iodosulfuron (3.73% year−1) and ACCase inhibitors sethoxydim/cycloxydim (1.92% year−1) and fenoxaprop (1.36% year−1) than for the substituted urea chlorotoluron (0.69% year−1) and the dinitroaniline pendimethalin (1.10% year−1). These results are consistent with other studies on the relative resistance risk associated with these different modes of action. There was also a surprisingly good correlation between results for random and non‐random resistance testing, which has also been found in studies with other weed species in Canada and Australia. This indicates that routine testing of submitted samples can replace, at least partly, the need for random surveys which tend to be both labour intensive and expensive. These results, compiled over 35 years, show the value of routine resistance screening, not only for detecting resistance at the individual field level (‘micro’ scale), but also the distribution, evolution and impact of resistance country‐wide (‘macro’ scale). However, it is important that standardised testing methods, including appropriate reference populations, are used by different testing centres to ensure consistent results.

Soybean seed composition prediction from standing crops using PlanetScope satellite imagery and machine learning

Soybean is a pivotal agricultural commodity around the world, primarily because of its high seed protein and oil concentration. Therefore, farmers, breeders and end-users are highly interested in understanding and predicting the soybean seed composition traits from the individual field level or agroecosystem. Seed composition traits are the proportions of different chemical and physical makeup of soybean seeds. Frequent daily coverage of PlanetScope (PS) satellite provides a unique opportunity of estimating seed composition due to its ability to track crop growth and development with its unique combination of high spatial and temporal resolution. We aim to predict six different soybean seed composition traits (i.e., protein, oil, sucrose, fiber, ash, starch) using PS imagery of standing soybean crops and machine learning algorithms. We developed multi-stream deep neural network which is based on two types of recurrent neural networks, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) that utilize temporal phenology observed from PS. Four statistical machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVR) were used for comparison. Our results show that GRU worked well for protein (R2 0.36, NRMSE 3.62%) and oil (R2 0.53, NRMSE 4.78%), SVR showed the best results for sucrose (R2 0.74, NRMSE 8.34%), fiber (R2 0.21, NRMSE 4.20%), and starch (R2 0.15, NRMSE 16.84%), and PLSR provided the best result for ash (R2 0.60, NRMSE 1.70%). Among the features, vegetation indices at later reproductive stages were found as the most important variables compared to texture features. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation.

Multi-Seed Zea Pellets (MSZP) for increasing agroecosystem biodiversity

AbstractIntensive agricultural crop production is typically associated with low biodiversity. Low biodiversity is associated with a deficit of ecosystem services, which may limit crop yield (e.g., low pollination of insect-pollinated crops) at the individual field level or exacerbate the landscape-level impacts of intensive agriculture. To increase biodiversity and enhance ecosystem services with minimal loss of crop production area, farmers can plant desirable non-crop species near crop fields. Adoption of this practice is limited by inefficiencies in existing establishment methods. We have developed a novel seed-molding method allowing non-crop species to be planted with a conventional corn (Zea mays L.) planter, reducing labor and capital costs associated with native species establishment. Common milkweed (Asclepias syriaca L.) was selected as a model native species, because Asclepias plants are the sole food source for monarch butterfly (Danaus plexippus L.) larvae. Stratified A. syriaca seeds were added to a mixture of binder (maltodextrin) and filler (diatomaceous earth and wood flour) materials in a 3D-printed mold with the dimensions of a corn seed. The resulting Multi-Seed Zea Pellets (MSZP), shaped like corn seeds, were tested against non-pelleted A. syriaca seeds in several indoor and outdoor pot experiments. Molding into MSZP did not affect percent emergence or time to emergence from a 2-cm planting depth. Intraspecific competition among seedlings that emerged from an MSZP did not differ from competition among seedlings that emerged from a cluster of non-pelleted seeds. These findings demonstrate the potential of MSZP technology as a precise and efficient method for increasing agroecosystem biodiversity.

Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment

Methods based on Sentinel-1 data were developed to monitor crops and fields to facilitate the distribution of subsidies. The objectives were to (1) develop a methodology to predict individual crop species or or management regimes; (2) investigate the earliest time point in the growing season when the species predictions are satisfactory; and (3) to present a method to assess the uncertainty of the predictions at an individual field level. Seventeen Sentinel-1 synthetic aperture radar (SAR) scenes (VV and VH polarizations) acquired in interferometric wide swath mode from 14 May through to 30 August 2017 in the same geometry, and selected based on the weather conditions, were used in the study. The improved k nearest neighbour estimation, ik-NN, with a genetic algorithm feature optimization was tailored for classification with optional Sentinel-1 data sets, species groupings, and thresholds for the minimum parcel area. The number of species groups varied from 7 to as large as 41. Multinomial logistic regression was tested as an optional method. The Overall Accuracies (OA) varied depending on the number of species included in the classification, and whether all or not field parcels were included. OA with nine species groups was 72% when all parcels were included, 81% when the parcels area threshold (for incorporating parcels into classification) was 0.5 ha, and around 90% when the threshold was 4 ha. The OA gradually increased when adding extra Sentinel-1 scenes up until the early August, and the initial scenes were acquired in early June or mid-May. After that, only minor improvements in the crop recognition accuracy were noted. The ik-NN method gave greater overall accuracies than the logistic regression analysis with all data combinations tested. The width of the 95% confidence intervals with ik-NN for the estimate of the probability of the species with the largest probability on an individual parcel varied depending on the species, the area threshold of the parcel and the number of the Sentinel-1 scenes used. The results ranged between 0.06–0.08 units (6–8% points) for the most common species when the Sentinel-1 scenes were between 1 June and 12 August. The results were well-received by the authorities and encourage further research to continue the study towards an operational method in which the space-borne SAR data are a part of the information chain.

Characterizing Crop Water Use Dynamics in the Central Valley of California Using Landsat-Derived Evapotranspiration

Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and agricultural production. The main objective of this paper is to characterize the spatiotemporal dynamics of crop water use in the Central Valley of California using Landsat-based annual actual evapotranspiration (ETa) from 2008 to 2018 derived from the Operational Simplified Surface Energy Balance (SSEBop) model. Crop water use for 10 crops is characterized at multiple scales. The Mann–Kendall trend analysis revealed a significant increase in area cultivated with almonds and their water use, with an annual rate of change of 16,327 ha in area and 13,488 ha-m in water use. Conversely, alfalfa showed a significant decline with 12,429 ha in area and 13,901 ha-m in water use per year during the same period. A pixel-based Mann–Kendall trend analysis showed the changing crop type and water use at the level of individual fields for all of Kern County in the Central Valley. This study demonstrates the useful application of historical Landsat ET to produce relevant water management information. Similar studies can be conducted at regional and global scales to understand and quantify the relationships between land cover change and its impact on water use.

A perspective on management of Helicoverpa armigera: transgenic Bt cotton, IPM, and landscapes.

Helicoverpa armigera is a major pest of agriculture, horticulture and floriculture throughout the Old World and recently invaded parts of the New World. We overview of the evolution in thinking about the application of area-wide approaches to assist with its control by the Australian Cotton Industry to highlight important lessons and future challenges to achieving the same in the New World. An over-reliance of broad-spectrum insecticides led to Helicoverpa spp. in Australian cotton rapidly became resistant to DDT, synthetic pyrethroids, organophosphates, carbamates and endosulfan. Voluntary strategies were developed to slow the development of insecticide resistance, which included rotating chemistries and basing spray decisions on thresholds. Despite adoption of these practices, insecticide resistance continued to develop until the introduction of genetically modified cotton provided a platform for augmenting Integrated Pest Management in the Australian cotton industry. Compliance with mandatory resistance management plans for Bt cotton necessitated a shift from pest control at the level of individual fields or farms towards a coordinated area-wide landscape approach. Our take-home message for control of H. armigera is that resistance management is essential in genetically modified crops and must be season long and area-wide to be effective. © 2016 Society of Chemical Industry.

Dry sowing increases farm level wheat yields but not production risks in a Mediterranean environment

Dry sowing is the practice of sowing a crop into a dry seed bed before the autumn rains. This is in contrast with the traditional practice of wet sowing where seed is placed into a moist seed bed following a rainfall event in autumn. We evaluated the putative benefits and risks of dry sowing for different soils and locations in Western Australia (WA) using a multi-field (15 or 30 day sowing programmes), multi-year (54 seasons) simulation model analysis with the Agricultural Production Systems Simulator. Importantly our analysis evaluated dry sowing at the farm rather than the field level. Dry sowing has increased in WA, in response to reduced rainfall, increased variation in the timing and amount of autumn rainfall and increased farm areas. Dry sowing is considered beneficial because it can increase yields, make better use of available machinery/labour and decrease heat stress during grain filling due to earlier flowering. Perceived risks of dry sowing include early season water deficit and frost around anthesis. There were large potential yield benefits of up to 35% with dry sowing compared to wet sowing. The largest yield benefits were for heavy soils, drier locations, and larger cropping programmes. Yield gains were greatest in seasons with low to medium yield potential (300–2500 kg/ha). In seasons with a late start to the opening rains dry sowing brought the sowing date of the last field sown earlier compared with wet sown programmes, with a clear yield benefit for early sowing. Dry sowing also allowed larger farm areas to be sown consistently with equivalent machinery capacity and labour availability. The results highlighted that growing-season (May–Oct) rainfall still set the upper limit to yield but that by practicing dry sowing farms are more likely to yield close to the water-limited benchmark. Dry sown farms produced an average of 350 kg/ha less than the water-limited benchmark and the wet sown cropping programmes 960 kg/ha less. At most sites the risks of a soil water deficit during seedling establishment more than doubled in a dry sown cropping programme compared with wet sowing. Dry sowing resulted in a very small increase (maximum 4%) in the proportion of crop frosted at anthesis. In contrast, dry sowing markedly reduced the proportion of crop that was exposed to heat events during grain filling. The analysis has demonstrated that single field simulation models can be used to evaluate management strategies applied at the individual field level but that influence whole farm productivity. Dry sowing is an appropriate strategy to manage yield risk by increasing grain yields with a minimal increase of production risks.

Assessing irrigation scheme water use and farmers’ performance using wireless telemetry systems

Assessing irrigation performance is essential for judicious water management in agriculture. A wireless telemetry system was used to collect water use records at the individual field level during two irrigation seasons in the Genil – Cabra Irrigation Scheme (GCIS), Spain. A total of 1365 plots were monitored, encompassing around 7500ha devoted mainly to wheat, olive, cotton, sunflower, garlic and maize, and served by an on-demand pressurized irrigation system. Substantial variability in field water usage among farmers was detected in the irrigation management of different crops within the GCIS, based on the detailed water use records. The variability detected within each crop included variations in the amount of water use as well as in the timing of water applications. Using the information provided by the telemetry system, a new graphical irrigation performance assessment based on variability analysis is proposed. Finally, in an attempt to explain the observed variability, interviews were carried out with farmers and their responses indicated that much of the variability was caused by variations in their approaches to deficit irrigation. In the case of olive orchards, tree age was an important element explaining the variability in water amounts.

Crop Seeding Level: Implications for Weed Management in Sweet Corn

Sweet corn is seeded under a wide range of population densities; however, the extent to which variable population density influences weed suppression is unknown. Therefore, field studies were undertaken to quantify the influence of sweet corn seeding level on growth, seed production, and post-harvest seed germination of wild-proso millet, one of the most problematic weeds in the crop. As crop seeding level increased, path analysis results indicated the crop canopy became taller and thicker, resulting in less wild-proso millet biomass, seed production, and germinability. However, at the level of individual fields, reductions in wild-proso millet growth and seed production were modest, at best, between a crop population currently used by growers and a higher crop population known to optimize yield of certain hybrids. These results indicate near-future increases in sweet corn seeding levels may play a minor role in improving weed management in individual sweet corn fields. Nonetheless, a reduction in crop populations, via weather- or management-driven phenomenon, increases risk of greater wild-proso millet seed production.

A review of the effects of crop agronomy on the management of Alopecurus myosuroides

SummaryThis study reviews 52 field experiments, mostly from the UK, studying the effects of cultivation techniques, sowing date, crop density and cultivar choice on Alopecurus myosuroides infestations in cereal crops. Where possible, a statistical meta‐analysis has been used to calculate average responses to the various cultural practices and to estimate their variability. In 25 experiments, mouldboard ploughing prior to sowing winter cereals reduced A. myosuroides populations by an average of 69%, compared with non‐inversion tillage. Delaying drilling from September to the end of October decreased weed plant densities by approximately 50%. Sowing wheat in spring achieved an 88% reduction in A. myosuroides plant densities compared with autumn sowing. Increasing winter wheat crop density above 100 plants m−2 had no effect on weed plant numbers, but reduced the number of heads m−2 by 15% for every additional increase in 100 crop plants, up to the highest density tested (350 wheat plants m−2). Choosing more competitive cultivars could decrease A. myosuroides heads m−2 by 22%. With all cultural practices, outcomes were highly variable and effects inconsistent. Farmers are more likely to adopt cultural measures and so reduce their reliance on herbicides, if there were better predictions of likely outcomes at the individual field level.

The economic environment and the growth in reserves from known gas fields in the Gulf of Mexico

Abstract The vast proportion of yearly oil and gas reserve additions are accounted for by reserve changes in known oil and gas fields. For example, in 2008 approximately 96% of the dry natural gas reserve additions in the United States were accounted for by reserve changes in known fields. The increase in proved ultimate recovery that results from these reserve changes is known as reserve appreciation or reserve growth. Ideally, reserve growth should be measured by comparing the current known petroleum volumes equal to cumulative production plus remaining reserves of fields discovered in say, 1950, with their initial discovery sizes. Unfortunately, implementing the ideal is often not possible since the initial discovery sizes of fields discovered before 1975, or thereabouts, are many times unknown. Up to now, the standard solution to the problem has been based largely on the work of Arrington (1960) who developed a method to estimate reserve growth when initial discovery sizes are unknown and the number of time series observations is small. While the contribution of the economic environment to the growth process has been acknowledged by geologists, the application of the Arrington method presumes that growth is independent of the economic environment. In our view, this creates a bias. For example, if, as we suspect, the economic environment affects reserve growth, then an Arrington-based supply analysis will underestimate the response of supplies to higher prices. In this paper, we employ a rich database from the former United States Minerals Management Service (MMS) to evaluate the contribution of the economic environment to the reserve growth process. In contrast to Forbes and Zampelli (2009) which examined reserve growth at the individual field level, the analysis in this paper aggregates the field level data based on the year of first production. One advantage of this approach is that it has the potential to yield insights about the resource potential of a particular geologic province. The results strongly suggest that age is not the sole factor in explaining a field’s annual reserve growth. In particular, we find that the annual growth rate in the known petroleum volumes of a field is affected by the economic environment as proxied by price. Incorporation of this effect into the modeling process has the potential to improve the accuracy of resource assessments.

Economies of Density versus Natural Advantage: Crop Choice on the Back Forty

Abstract We estimate the factors determining specialization of crop choice at the level of individual fields, distinguishing between the role of natural advantage (soil characteristics) and economies of density (scale economies achieved when farmers plant neighboring fields the same). Using rich geographic data from North Dakota, including new data on crop choice collected by satellite, we estimate a model of how a farmer plants adjacent fields under the farmer's control. We find planting decisions on a field are heavily dependent on the soil characteristics of adjacent fields. Through this relationship, we back out the structural parameters of economies of density.

Communicating complexity: Integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development

African farming systems are highly heterogeneous: between agroecological and socioeconomic environments, in the wide variability in farmers’ resource endowments and in farm management. This means that single solutions (or ‘silver bullets’) for improving farm productivity do not exist. Yet to date few approaches to understand constraints and explore options for change have tackled the bewildering complexity of African farming systems. In this paper we describe the Nutrient Use in Animal and Cropping systems – Efficiencies and Scales (NUANCES) framework. NUANCES offers a structured approach to unravel and understand the complexity of African farming to identify what we term ‘best-fit’ technologies – technologies targeted to specific types of farmers and to specific niches within their farms. The NUANCES framework is not ‘just another computer model’! We combine the tools of systems analysis and experimentation, detailed field observations and surveys, incorporate expert knowledge (local knowledge and results of research), generate databases, and apply simulation models to analyse performance of farms, and the impacts of introducing new technologies. We have analysed and described complexity of farming systems, their external drivers and some of the mechanisms that result in (in)efficient use of scarce resources. Studying sites across sub-Saharan Africa has provided insights in the trajectories of change in farming systems in response to population growth, economic conditions and climate variability (cycles of drier and wetter years) and climate change. In regions where human population is dense and land scarce, farm typologies have proven useful to target technologies between farmers of different production objectives and resource endowment (notably in terms of land, labour and capacity for investment). In such regions we could categorise types of fields on the basis of their responsiveness to soil improving technologies along soil fertility gradients, relying on local indicators to differentiate those that may be managed through ‘maintenance fertilization’ from fields that are highly-responsive to fertilizers and fields that require rehabilitation before yields can improved. Where human population pressure on the land is less intense, farm and field types are harder to discern, without clear patterns. Nutrient cycling through livestock is in principle not efficient for increasing food production due to increased nutrient losses, but is attractive for farmers due to the multiple functions of livestock. We identified trade-offs between income generation, soil conservation and community agreements through optimising concurrent objectives at farm and village levels. These examples show that future analyses must focus at farm and farming system level and not at the level of individual fields to achieve appropriate targeting of technologies – both between locations and between farms at any given location. The approach for integrated assessment described here can be used ex ante to explore the potential of best-fit technologies and the ways they can be best combined at farm level. The dynamic and integrated nature of the framework allows the impact of changes in external drivers such as climate change or development policy to be analysed. Fundamental questions for integrated analysis relate to the site-specific knowledge and the simplification of processes required to integrate and move from one level to the next.

Geographical variation in resistance to acetyl‐coenzyme A carboxylase‐inhibiting herbicides across the range of the arable weed Alopecurus myosuroides (black‐grass)

*The geographical structure of resistance to herbicides inhibiting acetyl-coenzyme A carboxylase (ACCase) was investigated in the weed Alopecurus myosuroides (black-grass) across its geographical range to gain insight into the process of plant adaptation in response to anthropogenic selective pressures occurring in agricultural ecosystems. *We analysed 297 populations distributed across six countries in A. myosuroides' main area of occupancy. The frequencies of plants resistant to two broadly used ACCase inhibitors and of seven mutant, resistant ACCase alleles were assessed using bioassays and genotyping, respectively. *Most of the resistance was not endowed by mutant ACCase alleles. Resistance and ACCase allele distribution patterns were characterized by mosaicism. The prevalence of resistance and of ACCase alleles differed among countries. *Resistance clearly evolved by redundant evolution of a set of resistance alleles or genes, most of which remain unidentified. Resistance in A. myosuroides was shaped by variation in the herbicide selective pressure at both the individual field level and the national level.

A spatiotemporal model of shifting cultivation and forest cover dynamics

ABSTRACTSustainable use of humid forest resources as a source of fertile land for cultivation requires long periods of fallow and the ability to move the zone of active cultivation from one location to another over time. At the individual field level, shifting cultivation is essentially a resource extraction problem akin to a pulse fishery – a short period of intensive use of the stock of soil fertility followed by a long idle period permitting regeneration. This paper describes a spatiotemporal model of resource extraction adapted to the use of forest resources by shifting cultivators. Theoretically grounded in the spatial and household modelling literature, it is a structural simulation model of household decision-making, and includes a demonstration of the concept with a limited data set from southern Cameroon. Use of a stated preference approach to modelling decision-making identifies individual preferences and spatial path-dependency as important sources of shortened fallows and resource degradation.

Predicting and Mapping Herbicide–Soil Partition Coefficients for EPTC, Metribuzin, and Metolachlor on Three Colorado Fields

Abstract Understanding the spatial variability of herbicide sorption to soil is important in determining the bioavailability as well as leaching potential of the chemical across a field. Multiple methods have been used to estimate herbicide sorption variability at the macroscale, but it has been difficult to measure soil heterogeneity or herbicide sorption at the individual field level. One method to determine soil heterogeneity is to create zones within a field based on maps of the apparent bulk soil electrical conductivity (ECa). These zones can be used to direct soil sampling to determine the fraction of organic carbon (foc) of each zone. The foc, in turn, can be used to predict the variability of herbicide binding among zones. Surface (0 to 30 cm) bulk-soil electrical conductivity (ECs) maps were made for three sandy fields in eastern Colorado, and soil samples were taken from the ECs zones within each field. The foc, and the soil–water partition coefficient (Kd) for EPTC, metribuzin, and metolachlor ...

Providing Database Encryption As A ScalableEnterprise Infrastructure Service

As databases become networked in more complex multi-tiered applications, their vulnerability to external attack grows. We address scalability as a particularly vital problem and propose alternative solutions for data encryption as an enterprise IT infrastructure component. In this paper we explore a new approach for data privacy and security in which a security administrator protecting privacy at the level of individual fields and records, and providing seamless mechanisms to create, store, and securely access databases. Such a model alleviates the need for organizations to purchase expensive hardware, deal with software modifications, and hire professionals for encryption key management development tasks. Although access control has been deployed as a security mechanism almost since the birth of large database systems, many still look at database security as a problem to be addressed as the need arises – this is often after threats to the secrecy and integrity of data have occurred. Instead of building walls around servers or hard drives, a protective layer of encryption is provided around specific sensitive data items or objects. This prevents outside attacks as well as infiltration from within the server itself. This also allows the security administrator to define which data stored in databases are sensitive and thereby focusing the protection only on the sensitive data, which in turn minimizes the delays or burdens on the system that may occur from other bulk encryption methods.

Providing Database Encryption as a Scalable Enterprise Infrastructure Service

As databases become networked in more complex multi-tiered applications, their vulnerability to external attack grows. We address scalability as a particularly vital problem and propose alternative solutions for data encryption as an enterprise IT infrastructure component. In this paper, we explore a new approach for data privacy and security in which a security administrator protecting privacy at the level of individual fields and records, and providing seamless mechanisms to create, store, and securely access databases. Such a model alleviates the need for organizations to purchase expensive hardware, deal with software modifications, and hire professionals for encryption key management development tasks. Although access control has been deployed as a security mechanism almost since the birth of large database systems, many still look at database security as a problem to be addressed as the need arises - this is often after threats to the secrecy and integrity of data have occurred. Instead of building walls around servers or hard drives, a protective layer of encryption is provided around specific sensitive data items or objects. This prevents outside attacks as well as infiltration from within the server itself. This also allows the security administrator to define which data stored in databases are sensitive and thereby focusing the protection only on the sensitive data, which in turn minimizes the delays or burdens on the system that may occur from other bulk encryption methods. Encryption can provide strong security for data at rest, but developing a database encryption strategy must take many factors into consideration. Different stored data encryption strategies are outlined, so you can decide the best practice for each situation, and each individual field in your database, to handle different security and operating requirements. Application code and database schemas are sensitive to changes in the data type and data length. the paper presents a policy driven solution that allows transparent data level encryption that does not change the data field type or length.

A digital model of pattern and productivity in an agroforestry landscape

Agroforestry is an approach to land-use which offers much promise in addressing many of the inter-related problems associated with rural development and land degradation ( Scherr, 1992; Nair, 1993). This research incorporates remotely sensed agroforestry landscape information within a computerized model that relates spatial structure at the field and landscape scale to maize production. Remote sensing, cartographic modeling as well as landscape ecological theory and methods of analysis figure prominently in the work. The study area is in the coffee zone on the southern footslopes of Mount Kenya. The research tests the hypotheses that: there is a correlation between landscape, field structure and crop production. Specifically, it is hypothesized that spatial configuration at the field scale, (e.g. patch size, shape and adjacency relationships etc.) as well as at the landscape scale (density of trees and hedgerows) can be significantly correlated with productivity. The central hypothesis is that spatial variability across the landscape, at scales both at and beyond that of the field, can explain a significant amount of the variability in yields. In order to carry out the work, a recent innovation in remote sensing, the Compact Airborne Spectrographic Imager (CASI), has been used in the project. The CASI was used to acquire a detailed digital image of the site at a high spatial resolution (1.75 m pixel size). Six spectrally narrow width bands were acquired with the purpose of determining chlorophyll density as well as crop type and vigor. Findings indicate that the spatial measures that were undertaken at the landscape scale do not explain enough crop variability to be statistically significant. Rather, it is far better explained at the individual field level. Here it was demonstrated that the distance from shelter plantings is inversely and curvilinearly related to crop vigor as measured by the relationship between the Near Infra Red and Red wavelengths. Findings indicate that the positive effects of trees in the study landscape are limited to within 25 m of the canopy and may increase production as much as 32% at the optimal distance. An optimum hedgerow inter-distance of 40 m is suggested in order to maximize crop vigor. The impact of both the existing conditions as well as two possible alternatives are evaluated for their total landscape wide impacts on maize yields and wood production. It was determined that removal of the existing hedgerows (50% of all the trees in the area) by a disturbance such as a pest outbreak would cause a 5% reduction in maize production. A second alternative, which identified under-utilized planting niches throughout the landscape, is predicted to result in a modest increase in maize production and a doubling of wood products.

Nutrients in the West African Sudano‐Sahelian zone: Losses, transfers and role of external inputs

AbstractCrop growth on acid sandy soils of the Sudano‐Sahelian zone is primarily limited by the low amounts of organic matter and available mineral nutrients in the topsoil. The shortening of fallow periods with population growth, the exploitation of fire wood, spatial nutrient transfers by wind and water and net nutrient exports with crop yields in the order of 15 kg nitrogen (N) 2 kg phosphorus (P) and 15 kg potassium (K) ha−1 yr−1 for traditional fields planted to pearl millet (Pennisetum glaucum L.) in the southern Sahel have exhausted these resources. Large productivity declines in the prevailing agro‐pastoral systems are the consequence. Data are presented to show bow nutrient exports at the level of individual fields, through grain yields and biomass removal as forage, firewood, construction material, and also through run‐off, wind and water erosion, leaching and volatilisation, are either losses for the ecosystem or sources of nutrients transferred within the ecosystem. Livestock is a vector of nutrients from rangelands to manured fields through forage intake and excretion and helps to shortcut nutrient cycles, but it is also a net consumer of organic matter and minerals. Through the effects of trampling on the soil and of grazing on the vegetation composition and production, livestock also indirectly affects the cycling and transfer of nutrients. Net nutrient inputs by heavy rains causing overland flow, dust deposition and through biological nitrogen fixation, are also unequally distributed and thus aggravate the fertility gradient from large nutrient ‘source’ areas such as rangelands to small ‘sink’ areas such as fallows, low lands, temporary lakes and river benches. At a finer resolution, shrubs and trees with their alternating periods of nutrient storage and recycling in leaves and wood, micro‐depressions, termite mounts and ant nests become localised points of nutrient concentration and high crop productivity. To balance losses of nutrients in these integrated systems and to obtain sustainable increases in production, external inputs of nutrients are necessary. These may be introduced by two different pathways, either via mineral fertilisers applied to croplands or via externally produced supplements fed to livestock. These pathways are complementary and each one has direct effects on the other. Fertiliser application to croplands affect livestock through the increase in available forage and crop residue feed, whereas feed supplementation affects crop production through higher amounts and better quality of manure. The adoption of either strategy by farmers requires a high nutrient use efficiency at low cost. Data from regional field trials revealed increases in total dry matter (TDM) of cereals with broadcast annual P application at 13 kg ha−1 ranging across three years from 19 to 88% for rockphosphate and from 34 to 102% for single superphosphate (SSP). Still, a low‐external input approach seemed more advantageous to farmers. The placement of NPK fertiliser at 4 kg P ha−1 with the seed at or shortly after planting caused average TDM increases of 70% for millet, sorghum, maize, cowpea and groundnut and showed an up to three‐fold higher phosphorus use efficiency than broadcast P. On the other hand, late dry season supplementation of grazing steers with millet bran not only decreased animal weight losses but also increased N and P concentrations in the faeces. The larger nutrient concentrations in the manure increased millet grain yield by 28% and stover yield by 21% on a field manured at a rate of 3 t DM ha−1. However, even with such approaches the current trends of declining soil productivity can only be reversed, if local policy makers are willing to support agricultural change through better terms of trade for agricultural products.