A reviewer for the Journal of the Royal Statistical Society of England comments \"This is the first book covering in one volume all important topics in genetical statistics.\" Written to provide basic probability ideas in terms of genetic situations, since the theory of genetics is a probability theory; to give a definitive treatment of applications of these ideas to genetic theory; and to describe statistical methods appropriate to the data models that are developed.

An Introduction to Genetic Statistics

Aim To assemble a data set of global crop planting and harvesting dates for 19 major crops, explore spatial relationships between planting date and climate for two of them, and compare our analysis with a review of the literature on factors that drive decisions on planting dates.

Location  Global.

Methods  We digitized and georeferenced existing data on crop planting and harvesting dates from six sources. We then examined relationships between planting dates and temperature, precipitation and potential evapotranspiration using 30-year average climatologies from the Climatic Research Unit, University of East Anglia (CRU CL 2.0).

Results  We present global planting date patterns for maize, spring wheat and winter wheat (our full, publicly available data set contains planting and harvesting dates for 19 major crops). Maize planting in the northern mid-latitudes generally occurs in April and May. Daily average air temperatures are usually c. 12–17 °C at the time of maize planting in these regions, although soil moisture often determines planting date more directly than does temperature. Maize planting dates vary more widely in tropical regions. Spring wheat is usually planted at cooler temperatures than maize, between c. 8 and 14 °C in temperate regions. Winter wheat is generally planted in September and October in the northern mid-latitudes.

Main conclusions  In temperate regions, spatial patterns of maize and spring wheat planting dates can be predicted reasonably well by assuming a fixed temperature at planting. However, planting dates in lower latitudes and planting dates of winter wheat are more difficult to predict from climate alone. In part this is because planting dates may be chosen to ensure a favourable climate during a critical growth stage, such as flowering, rather than to ensure an optimal climate early in the crop's growth. The lack of predictability is also due to the pervasive influence of technological and socio-economic factors on planting dates.

Crop planting dates: an analysis of global patterns.

Species of Orobanche and Striga are among the most damaging parasitic weed species worldwide, but there are few reliable statistics on the full extent of the economic losses they cause. The distribution, host range and economic importance of the major species of Orobanche and Striga are briefly summarised. A review of literature over the period since 1991 suggests that many million hectares are infested and that the losses amount to $ US billions annually. Unfortunately there are almost no fully reliable figures on which to base these figures precisely. Meanwhile, there is little evidence of any significant change in intensity, range or losses caused over this period. Any reduction in the importance of these damaging weeds is sporadic, and alleviation of the problems is mostly localised. Furthermore, while the importance of Orobanche species may be broadly static, Striga species on cereals continue to become more serious in many countries owing to continued loss of soil fertility. It is suggested that new techniques may be needed for measurement of the extent of losses caused by these genera and their economic impact. There is continued urgency to develop control measures appropriate to the farming systems involved, and to reduce the risk of spread of both groups of parasite to new areas.

Observations on the current status of Orobanche and Striga problems worldwide

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

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High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

Agricultural water scarcity in the predominantly rainfed agricultural system of sub-Saharan Africa (SSA) is more related to the variability of rainfall and excessive non-productive losses, than the total annual precipitation in the growing season. Less than 15% of the terrestrial precipitation takes the form of productive ‘green’ transpiration. Hence, rainwater harvesting and management (RWHM) technologies hold a significant potential for improving rainwater-use efficiency and sustaining rainfed agriculture in the region. This paper outlines the various RWHM techniques being practiced in SSA, and reviews recent research results on the performance of selected practices. So far, micro-catchment and in situ rainwater harvesting techniques are more common than rainwater irrigation techniques from macro-catchment systems. Depending on rainfall patterns and local soil characteristics, appropriate application of in situ and micro-catchment techniques could improve the soil water content of the rooting zone by up to 30%. Up to sixfold crop yields have been obtained through combinations of rainwater harvesting and fertiliser use, as compared to traditional practices. Supplemental irrigation of rainfed agriculture through rainwater harvesting not only reduces the risk of total crop failure due to dry spells, but also substantially improves water and crop productivity. Depending on the type of crop and the seasonal rainfall pattern, the application of RWHM techniques makes net profits more possible, compared to the meagre profit or net loss of existing systems. Implementation of rainwater harvesting may allow cereal-based smallholder farmers to shift to diversified crops, hence improving household food security, dietary status, and economic return. The much needed green revolution and adaptations to climate change in SSA should blend rainwater harvesting ideals with agronomic principles. More efforts are needed to improve the indigenous practices, and to disseminate best practices on a wider scale.

Rainwater harvesting and management in rainfed agricultural systems in sub-Saharan Africa - A review

Infestation of crop fields by Striga species in the savanna zones of northeast Nigeria.

Corn (Zea mays L.) production is gradually spreading into the Sudan savanna zone of West Africa where production is limited by erratic and inadequate rainfall. To increase corn production, production practices should be properly designed to minimize the effects of low precipitation and high temperatures that characterize the zone. A study, to determine the performance of late (120 d), early (90 d), and extra-early maturing (80 d) corn cultivars over a range of planting dates, was performed in the Sudan savannas of northeast Nigeria. Delaying planting generally increased days to flowering and the anthesis-silking interval (ASI) and reduced dry matter production and yield and yield components. In Azir, planting of corn on 13 July reduced grain yield by 42% in 2006 because of a dry spell during crop establishment. Delaying planting to 21 and 28 July also reduced grain yield by 19 and 28.5%, respectively over the 2 yr. Averaged over the 2-yr yield reduction was 29.5 and 42% when corn was planted on 21 and 28 July, respectively in Damboa. There was no interaction between planting date and corn cultivar for days to silking, ASI, and grain yield suggesting that the cultivars responded similarly to planting date. The extra-early maturing cultivar, 95 TZEE-W, produced highest dry matter, harvest index, and grain yield at all planting dates suggesting that this cultivar is the most suitable in both locations. To reduce risk of drought stress, extra-early maturing corn cultivars should be planted in the Sudan savanna between the last week of June and the first week of July.

Planting date and cultivar effects on grain yield in dryland corn production

A 2-year ﬁeld study was conducted to assess the effect of weed competition on the performance ofupland rice cultivars in the savannas of Nigeria. A split-plot designwas used with three weeding levels asthe main plot treatments and the rice cultivars CG 14, ITA 150, WAB 56-104, NERICA1, NERICA2, andNERICA4 as the subplots. Grain yield, spikelets plant 1 and days to ﬂowering differed signiﬁcantly inplots weeded once or twice. Weed dry weight was negatively correlated with grain yield. Grain yield inall cultivars was over 50% less in the unweeded plots than in plots weeded once or twice. At Sabon-Gari,NERICA1 (unweeded or weeded twice), and NERICA4 (weeded once) were the most proﬁtable. At Tilla,NERICA4 was the most proﬁtable when weeded once or twice. Results showed that, given the proﬁt-ability levels under the different weeding regimes, farmers would have higher marginal returns if theygrew NERICA1 at Sabon-Gari and NERICA4 at Tilla and weed their rice plots twice.2008 Elsevier Ltd. All rights reserved.

Response of upland rice cultivars to weed competition in the savannas of

Response of upland rice cultivars to weed competition in the savannas of West Africa

Cowpea is an important food crop in the dry savannas of West and Central Africa because of its high protein content. Yields are, however, considerably reduced by the parasitic angiosperm Striga gesnerioides. Field trials over 2 years in two localities in northeast Nigeria evaluated the performance of diverse cowpea varieties under conditions of natural infestation by Striga. Grain yield was higher in Tilla than in Damboa where the higher Striga infestation may be caused by the lower rainfall and the sandy nature of the soils. Two varieties (IT97K-499-35 and IT90K-82-2) were confirmed to be resistant to Striga. Yield gain from IT97K-499-35 over the local variety ranged from 30% in Tilla in the northern Guinea savanna to 126% in Damboa in the Sudan savanna. The local variety Borno Brown and two improved varieties were found to be as susceptible to Striga as the susceptible control, TVX-3236, and therefore may not be recommended for cultivation in areas where Striga infestation is a problem. One variety, rep...

Lucky O. Omoigui

Papers

Infestation of crop fields by Striga species in the savanna zones of northeast Nigeria.

Planting date and cultivar effects on grain yield in dryland corn production

Response of upland rice cultivars to weed competition in the savannas of

Response of upland rice cultivars to weed competition in the savannas of West Africa

Field performance of improved cowpea varieties under conditions of natural infestation by the parasitic weed Striga gesnerioides